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I voted for option two because it is succinct and I believe encapsulates the other options. 

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This definition is from the National Association for Media Literacy Education (NAMLE).  I like the focus on critical thinking and making, the articulation of communication as part of the media literacy skill-set, plus the inclusion of active citizenship.

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Definition of media literacy:
“Media literacy is the ability to critically engage with media in all aspects of life. It is a form of lifelong literacy that is essential for full participation in society.”

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Everyday Googling: Results of an Observational Study and Applications for Teaching Algorithmic Literacy

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Everyday Googling: Results of an Observational Study and Applications for Teaching Algorithmic Literacy

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Abigail Bakke, 2020

https://www.sciencedirect.com/science/article/abs/pii/S8755461520300384?casa_token=oUYsUaxR_BcAAAAA:QPbk5tadnZPV6Z9Oweg5L6wEpQyniAeQYD1VHDoA4clTeO70nIKx3ioT5xqJk9BpM1NxmBhFeQ

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Everyday Googling: Results of an Observational Study and Applications for Teaching Algorithmic Literacy

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Students, and internet users generally, rely heavily on search engines for academic and personal research. Search engines, driven by algorithms, are s…

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EVERY CLICK YOU MAKE: ALGORITHMIC LITERACY AND THE DIGITAL LIVES OF YOUNG ADULTS

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EVERY CLICK YOU MAKE: ALGORITHMIC LITERACY AND THE DIGITAL LIVES OF YOUNG ADULTS

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https://spir.aoir.org/ojs/index.php/spir/article/view/11233 Abstract only, 2020

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Authors

Monica Jean Henderson University of Toronto, Canada

Leslie Regan Shade University of Toronto, Canada

Katie Mackinnon University of Toronto, Canada

DOI:
https://doi.org/10.5210/spir.v2020i0.11233

Keywords:
algorithms, digital literacy, young adults, media education, participatory methods

Abstract

Critical digital literacy comprises subsets of medium- and content-related skills necessary for digital privacy and digital citizenship. Frameworks for defining and evaluating digital literacy proliferate in academia and policymaking; however, in a networked climate subsumed by dataveillance, algorithmic bias, political bots, and deep fakes, these frameworks need to be updated. Algorithms may be the greatest determinant in sociopolitical online interactions and information gathering, and without a multivalent literacy of algorithms, nuanced understandings of digital privacy and digital citizenship may be unachievable. We therefore propose 'algorithmic literacy' become an essential element for digital literacy in young adult media education. Researchers have highlighted how intersectional aspects of gender, ability, and socioeconomic status are stronger predictors of low digital literacy than age. Following a tradition of participatory (rather than protectionist) research about youth privacy online, our research foregrounds young adults' practices and perspectives on algorithmic culture in order to co-develop a framework for algorithmic literacy. Our paper shares findings from a participatory project co-designing an algorithmic literacy toolkit with young adults as co-researchers and participants. We created a curriculum focusing on reviewing the current critical scholarly literature, policy, and popular discourse on algorithms. After two weeks of intensive research, our student co-researchers met amongst themselves to devise a sustainable, 'living-document' type of toolkit, comprising a website, an Instagram page, and a Medium blog. Reflected in the toolkit's name, The Algorithmic You uses an intersectional lens to facilitate peer-oriented 'self-discovery' of how algorithms shape and produce interactions in the everyday lives of young adults.

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EVERY CLICK YOU MAKE: ALGORITHMIC LITERACY AND THE DIGITAL LIVES OF YOUNG ADULTS

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Monica Jean Henderson University of Toronto, Canada Leslie Regan Shade University of Toronto, Canada Katie Mackinnon University of Toronto, Canada https://doi.org/10.5210/spir.v2020i0.11233 algorithms, digital literacy, young adults, media education, participatory methods Critical digital literacy comprises subsets of medium- and content-related skills necessary for digital privacy and digital citizenship.

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“New literacies” — can we agree on the basics?

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“New literacies” — can we agree on the basics?

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Blogpost, Newton, 2019
https://newscollab.org/2019/02/25/new-literacies-can-we-agree-on-the-basics/

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It's all about the capacity to find, understand, act on and create digital news and information. Agreement on the basics is a first step toward collaborating to expand 21st century literacies.

This blog post is an updated excerpt of News Co/Lab co-founder Eric Newton's report to the Knight Commission on Trust, Media and Democracy. Newton helped the commission develop its recommendations on literacy, including that all Americans by age 18 be "digitally literate citizens." Read the first post in the series here.

News stories in the digital age travel cyberspace as independent units, often divorced from the context of a newspaper or newscast. When everything looks more or less the same, it becomes harder to tell the difference between factual journalism and deliberate misinformation.

Most people are not news literate. Most can't say where the news comes from. News travels increasingly to us through social media, where most people share things without reading beyond a headline. People tend to trust stories not because of what they say but because they trust the person who sent them. They happily deliver falsehoods to each other.

Digital tools can help us deal with an impossibility of daily news and information choices. Yet most people don't know how to use them. This brings us to education: the long game. The Knight Foundation this month issued an important report calling for "moon shot" goals to increase the 21st century's "new literacies" and, to its credit, followed up with a major grant to the News Literacy Project.

But here's the rub. Roughly a decade ago, a different Knight Commission also found that the ideal of an informed and engaged community was not possible without literacy. It recommended the nation "integrate digital and media literacy as critical elements of education at all levels through collaboration among federal, state and local education officials."

That national initiative never happened.

Aside from the obvious roadblocks - political polarization, gridlock, money, a lack of popular support - some scholars argue that literacy proponents themselves present a roadblock because they do not, as a general rule, work together. Instead of a national push to master the new fundamentals of 21st century literacies, we see a variety of specialized approaches.

Yet all literacies come from the same family tree. They occupy different branches but have common roots. The fundamental themes of all the communication-related literacies are reflected in Article 19 of the Universal Declaration of Human Rights:

"Everyone has the right to freedom of opinion and expression; this right includes freedom to hold opinions without interference and to seek, receive and impart information and ideas through any media and regardless of frontiers."

Article 19 contains three of the four core elements of the "new literacies." We're talking about a world in which everyone, everywhere should be able to find, understand and create information and ideas in any media. The fourth element? To fulfill and maintain all other human rights, we must be free to, and know how to, act on information and ideas.

In the definitions below, the core activities - being able to find, understand, act on and create various forms of communication - are highlighted, an exercise to show that they exist in almost every definition. Agreeing on what the "new literacies" have in common would be, I think, a good step toward collaborating.

Information Literacy

Here's how the Association of College and Research Libraries, a division of the American Library Association, defined information literacy in the 2017 Framework for Information Literacy in Higher Education:

"Information literacy is a set of abilities requiring individuals to recognize when information is needed and have the ability to locate, evaluate, and use effectively the needed information."

Why do we need information literacy? Because, the ALA says, information increasingly "comes to individuals in unfiltered formats, raising questions about its authenticity, validity, and reliability ... information is available through multiple media, including graphical, aural, and textual, and these pose new challenges for individuals in evaluating and understanding it. The uncertain quality and expanding quantity of information pose large challenges for society. The sheer abundance of information will not in itself create a more informed citizenry without a complementary cluster of abilities necessary to use information effectively."

Digital Literacy

In 2011, the American Library Association's Digital Literacy Task Force defined digital literacy as " the ability to use information and communication technologies to find, evaluate, create, and communicate information, requiring both cognitive and technical skills. "

The task force focuses on communication as the way a person might act on information. A digitally literate person:

Possesses the variety of skills - technical and cognitive - required to find, understand, evaluate, create, and communicate digital information in a wide variety of formats;

Is able to use diverse technologies appropriately and effectively to retrieve information, interpret results, and judge the quality of that information;

Understands the relationship between technology, life-long learning, personal privacy, and stewardship of information;

Uses these skills and the appropriate technology to communicate and collaborate with peers, colleagues, family, and on occasion, the general public; and

Uses these skills to actively participate in civic society and contribute to a vibrant, informed, and engaged community.

Digital and Media Literacy

In her 2017 book, scholar Renee Hobbs, leader of the Media Education Lab at the University of Rhode Island, defines "Digital and Media Literacy" as: "the constellation of knowledge, skills and competencies necessary for thriving in a technology-saturated culture." She continues: "As information, entertainment, and persuasion are now shared digitally, and personal, social and professional relationships are developed through interaction with social media as well as mass media and popular culture, people of all ages need the ability to access, analyze, create, reflect and take action using a wide variety of digital tools, forms of expression and communication strategies."

Hobbs added the word "digital" to media literacy before many of her colleagues. She provided a similar and more detailed view of the "constellation of life skills" years earlier, saying "digital and media literacy" means a person can:

Make responsible choices and access information by locating and sharing materials and comprehending information and ideas.

Analyze messages in a variety of forms by identifying the author, purpose and point of view, and evaluating the quality and credibility of the content.

Create content in a variety of forms, making use of language, images, sound, and new digital tools and technologies.

Reflect on one's own conduct and communication behavior by applying social responsibility and ethical principles.

Take social action by working individually and collaboratively to share knowledge and solve problems in the family, workplace and community and by participating as a member of the community.

Media and Information Literacy

Globally, the United Nations Educational Scientific and Cultural Organization combined the fields of information and media literacy to develop its global Media and Information Literacy program. Instead of defining the term, the program guidelines outline " learning outcomes" and "main elements " that align with the fundamental four present in other definitions: the ability to find, understand, act on and create digital information. UNESCO also incorporates other new literacies: the overall context of information (media literacy), organizing and processing information (information literacy) and applying technological skills (digital literacy).

News Literacy

None of the preceding definitions emphasize the information Thomas Jefferson worried about, the independent information essential to self-government: news. A decade ago, current and former journalists created the specialty of "news literacy." Why another literacy? Many reasons. "Teaching to the test" was driving news out of the classroom, as was the country's increasing polarization. Newspaper in Education programs were being cut. The digital disruption of traditional media caused mass job losses and led to a local news crisis. Media literacy, while valuable, did not seem to appreciate the role of an ethical, independent, truth-seeking journalist.

A pioneering K-12 program, The News Literacy Project, sums up news literacy as the ability to tell fact from fiction, and says it has four "enduring understandings":

The ability to filter information is an essential skill for journalists and consumers.

The First Amendment and a free press are vital to American democracy.

Today's news and information ecosystem presents great challenges and enormous opportunities.

Knowing the standards of quality journalism empowers students as consumers and citizens.

The Center for News Literacy at Stony Brook University in New York offers its own definition, its mission statement echoing Thomas Jefferson: "a healthy civil society can exist only if the public is well-informed."

The center helps students understand a great deal about news, including to:

Recognize the difference between journalism and other kinds of information and between journalists and other purveyors of information;

Separate news from opinion in the context of journalism;

Analyze the differences between assertion and verification and between evidence and inference in a news report;

Evaluate and deconstruct news reports based on the quality of evidence presented and the reliability of sources, and to understand and apply these principles across all news platforms;

Distinguish between news media bias and audience bias.

Underlying these practices, says Stony Brook, are concepts such as understanding how smart news consumption can change lives, and our new responsibilities as publishers as well as consumers. The language is different, but the elements of finding, understanding, acting on and creating digital news and information are present in News Literacy.

Civic Literacy

Civic literacy is perhaps the most widely discussed (and widely defined) literacy. Generally, civic literacy involves understanding the role and functions of different levels of government, but also being able to act upon one's knowledge by participating in civic processes.

In 2003, the seminal Civic Mission of Schools report said civic education should help young people become "competent and responsible" citizens who are informed and thoughtful; participate in their communities; act politically; and have moral and civic virtues.

A 2011 follow-up report, Guardian of Democracy: The Civic Mission of Schools, said six "proven practices" make up a "high quality" civic learning experience: classroom instruction, discussion of current events and controversial issues, service-learning, extracurricular activities, school governance and simulations of democratic processes.

Of course, none of the above can happen without communication. Observing that media literacy is consolidating and "stretching" to include civic education - a topic widely discussed in the Knight Commission report - some scholars believe civic literacy can be reframed as a series of social practices along the lines of media literacy, such as " the ability to access, analyze, evaluate and create civic cultures. "

Other Literacies

Increasing civic literacy may well save America's democracy. But it is hardly the only topic-based literacy. Educators hope environmental literacy will save the planet, for example, and financial literacy will save the economy. And health literacy is literally a life and death matter. All of these specialties, and the many others that exist, focus on the find, understand, create and act on model.

Definitions: How they work together

Literacy definitions are moving closer together. In becoming more similar, each literacy is recognizing the importance of the others. Information and media literacy have added elements of each other and of digital literacy. Civic literacy and health literacy have added media literacy. News literacy and media literacy emphasize civic and social literacy.

Each one in its own way is trying to adapt to three realities: first, that the information explosion has created so much content that almost anything is available, if you know how to find it; second, that the interactive digital age of communication has turned consumers into people who share and create content, if you know how to do it well; and third, that information alone changes behavior only if you know how and desire to act on it.

It's understandable that the digital age definitions are growing. But that doesn't mean it is right or smart.

Each field that lays claim to a literacy is good at one of the fundamentals - librarians at finding information, for example, or civic teachers at showing how people can act on information. Despite what it thinks of itself, no field is good at the "literacy of everything."

An important question becomes whether each literacy's ambition to be the most comprehensive and important hinders efforts to work together to study and teach the "new fundamentals" of 21st-century literacies.

Suppose for a moment that what all students need in all grade levels are the four fundamentals of finding, understanding and acting on digital news and information. How does a debate over which literacy is "better" help them?

In the next post: Are the "controversies" about new literacies real?

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"New literacies" - can we agree on the basics?

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It's all about the capacity to find, understand, act on and create digital news and information. Agreement on the basics is a first step toward collaborating to expand 21st century literacies.

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News and Media Research Centre - Canberra University
Core learning outcomes:

critical thinking

strategies for use

participatory skills

understanding the media environment

reflection and lifelong learning

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Building algorithmic literacy for young people

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Building algorithmic literacy for young people

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https://www.digigen.eu/digigenblog/what-should-digital-literacy-look-like-in-an-age-of-algorithms-and-ai-neil-selwyn/

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By Neil Selwyn, professor at the faculty of education, Monash University, Melbourne, Australia

Children and young people have long been expected to develop digital skills and knowledge relevant to the technologies of their time. During the 1980s this took the form of 'computer literacy' and 'computer skills' such as learning basic programming commands and how to format floppy disks. In the 1990s, attention turned to 'internet literacy' and 'web skills' such as navigating hypertext and building web-pages. The 2000s and 2010s then saw the rise of 'cyber safety' and '21st century skills' such as online communication, creativity and collaboration. Most recently, we have seen calls for 'media & information literacy' skills relating to dealing with viral content, mis-information and fake news.

While the terminology might change, all these different 'digital literacies' are driven by a common aim of supporting children and young people to become informed, imaginative and savvy users of technology. As such, the DigiGen project is understandably interested in exploring what forms of digital literacy might be relevant to new generations of Europeans, and how digital literacy programmes can be developed for schools, families and other settings.

RETHINKING DIGITAL LITERACY FOR AN AGE OF ALGORITHMS AND AUTOMATION

We are now beginning to see arguments that digital technology is entering a new phase that requires us to start thinking about 'digital literacy' in markedly different terms. Whereas digital literacy efforts to date have focused on supporting young people to become better users of technology, the most significant digital technologies during the 2020s are likely not to be technology 'used by' people per se. Instead, the most influential and insidious digital technologies over the next few years are likely to be technologies that are 'used on' people.

We are talking here about the rise of data-driven automated technologies - what is often referred to as 'automated decision-making', algorithmic and 'AI' technologies. These are all systems and software that sit in the background of our everyday lives - monitoring, tracking our actions and movements, extracting data from our devices, and then using this information to make decisions. Algorithmic systems are already a common feature of young people's lives - dictating which targeted advertisement appears when a toddler watches a YouTube video, or perhaps whether a school leaver gets accepted to study at their preferred university.

As such, this calls for rethinking what might have been previously talked about as 'digital literacy' as a form of 'algorithmic literacy'. Some components of this might include:

recognising when data-driven automated systems are being used;

having a basic understanding of how these data-driven automated systems work - what Tania Bucher describes as an 'algorithmic imaginary';

knowing how to algorithmic systems - for example, writing with a natural language processing tool so that it helps (rather than hinders) your creativity;

knowing how to algorithmic systems - for example, using obfuscation tactics to avoid dataveillance;

recognising when human input and outsight is required - for example, knowing when to override an automated decision, or push back against algorithmic bias and automated discrimination.

Developing these new understandings and awareness are important for a number of reasons. On one hand, it is noted that employers are beginning to include familiarity with data and AI as a required employment skill across a range of general occupational areas. Much more importantly, dealing with algorithms is becoming a crucial part of contemporary citizenship. For example, Frank Pasquale talks about the importance of public education in order to counter the emergence of a ' black box society ', where automated decisions are made that citizens are certainly not part of, and usually not aware of.

Indeed, developing these new forms of algorithmic literacy is important if we are to avoid the emergence of new forms of digital inequality and disadvantage. Recent studies highlight the danger of algorithmic understanding being patterned strongly by socio-economic background. Thus, as with any form of digital literacy, young people need to be supported to ensure that algorithmic and automated technologies work in their interests (and not against their interests).

BUILDING ALGORITHMIC LITERACY IN YOUNG PEOPLE - SO WHAT NOW?

Of course, supporting the development of algorithmic literacy presents a challenge to families, educators, youth workers, and anyone else who works with children and young people. Algorithmic understandings and awareness cannot be simply taught in the same ways that one might be taught basic coding skills or how to choose safe passwords. Instead, these are issues that teachers, parents and youth workers need to 'co-learn' children and young people. Algorithmic literacy is a complex and opaque area, where even computer scientists and software developers responsible for developing these systems can be unsure how they actually work. So, this is an area of education that requires adults young people accepting that they all have lots to learn, and then being comfortable learning about it together.

There are already some interesting examples of what these literacy-building activities might look like. For example, there are some great practical examples of educators working with young people to develop toolkits that can support their algorithmic awareness - such as University of Toronto's ' The Algorithmic You' project. Similarly, there are some simple examples of students being supported to develop understandings of how search engine algorithms shape their research, background reading and eventual writing - with students then using these insights to rethink how to engage differently with search strategies and results. Above all, it is important that young people do not encounter algorithmic literacy as a dry abstract concept, but as a dynamic way to actively engage in shaping their digital futures. Alongside the obvious benefits of being able to better navigate digital systems, the development of algorithmic awareness might well lead to direct democratic involvement in the politics of the digital age. In short, algorithmic literacy is likely to be an increasingly important part of digital citizenship in the 2020s. Take, for example, recent cases of UK school students protesting against algorithmic injustices related to automated exam grading, and young people in Ireland protesting against algorithmic targeting of body image and mental health messages on social media. All told, algorithmic activism looks set to be a key part of young people's lives throughout the 2020s and beyond. These are definitely topics for everyone in the DigiGen community to continue thinking about over the next few years.

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What should 'digital literacy' look like in an age of algorithms and AI? Neil Selwyn - DigiGen

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By Neil Selwyn, professor at the faculty of education, Monash University, Melbourne, Australia Children and young people have long been expected to develop digital skills and knowledge relevant to the technologies of their time. During the 1980s this took the form of 'computer literacy' and 'computer skills' such as learning basic programming commands and how [...]

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Online misinformation and disinformation reports - current
https://www.newsguardtech.com/reports/

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A lot of data used in this topic is from an original study conducted in 2020. You can download the report and other material from Adult Media Literacy in Australia: Attitudes, Experiences & Needs
- report from N&MRC

Culturally and linguistically diverse (CALD) people are more active social media and online media users. For more interesting facts about CALD and media literacy, refer the infographic: Media Literacy in Australia - Culturally and Linguistically diverse (CALD) people

National Association for Media Literacy Education (NAMLE)'s Media Literacy Week is an annual convention showcasing contemporary research and educational programs. THis year it will follow themes: Access, Analyze, Evaluate, Create, and Act

References and further reading

From the Topic 1 presentation:

[Slide 6] Buckingham, D. (2003). Media Education: Literacy, Learning and Contemporary Culture. Cambridge, UK: Polity Press (p36)

[Slides 10-12] Notley, T., Chambers, S., Park, S. & Dezuanni, M. (2021). Adult Media Literacy in Australia: Attitudes, Experiences and Needs

[Slide 14] Park, S., McCallum, K., Holland, K., McGuinness, K., Lee, J., Fisher, C. & John, E. (2022). COVID-19: Australian News & Misinformation Longitudinal Study. Canberra: News Media Research Centre, University of Canberra.

Other:

Edwards, L. Stoilova, M., Anstead, N., Fry, A., El-Halaby, G. and Smith M. (2021) Rapid Evidence Assessment on Online Misinformation and Media Literacy: Final Report for Ofcom. www.ofcom.org.uk

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Question:
What results do I get compared to family/friends when we search the same thing on Google?

We get very similar results until you scroll further into the results list.

Left-wing results do not appear, likely because:

"They understood how search engine optimizers use sophisticated keywords and other techniques to game results, pushing some sites to the front of the line and more authoritative information to the back" (Bakke, 2020).

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Echo chambers, filter bubbles, polarisation

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Echo chambers, filter bubbles, polarisation

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https://reutersinstitute.politics.ox.ac.uk/echo-chambers-filter-bubbles-and-polarisation-literature-review

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Reuters Institute for the Study of Journalism

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DOI: 10.60625/risj-etxj-7k60

Executive summary ↑

Terms like echo chambers, filter bubbles, and polarisation are widely used in public and political debate but not in ways that are always aligned with, or based on, scientific work. And even among academic researchers, there is not always a clear consensus on exact definitions of these concepts.

In this literature review we examine, specifically, social science work presenting evidence concerning the existence, causes, and effect of online echo chambers and consider what related research can tell us about scientific discussions online and how they might shape public understanding of science and the role of science in society.

Echo chambers, filter bubbles, and the relationship between news and media use and various forms of polarisation has to be understood in the context of increasingly digital, mobile, and platform-dominated media environments where most people spend a limited amount of time with news and many internet users do not regularly actively seek out online news, leading to significant inequalities in news use.

When defined as a bounded, enclosed media space that has the potential to both magnify the messages delivered within it and insulate them from rebuttal, studies in the UK estimate that between six and eight percent of the public inhabit politically partisan online news echo chambers.

More generally, studies both in the UK and several other countries, including the highly polarised US, have found that most people have relatively diverse media diets, that those who rely on only one source typically converge on widely used sources with politically diverse audiences (such as commercial or public service broadcasters) and that only small minorities, often only a few percent, exclusively get news from partisan sources.

Studies in the UK and several other countries show that the forms of algorithmic selection offered by search engines, social media, and other digital platforms generally lead to slightly more diverse news use - the opposite of what the "filter bubble" hypothesis posits - but that self-selection, primarily among a small minority of highly partisan individuals, can lead people to opt in to echo chambers, even as the vast majority do not.

Research on polarisation offers a complex picture both in terms of overall developments and the main drivers and there is in many cases limited empirical work done outside the United States. Overall, ideological polarisation has, in the long run, declined in many countries but affective polarisation has in some, but not all, cases increased. News audience polarisation is much lower in most European countries, including the United Kingdom. Much depends on the specifics of individual countries and what point in time one measures change from and there are no universal patterns.

There is limited research outside the United States systematically examining the possible role of news and media use in contributing to various kinds of polarisation and the work done does not always find the same patterns as those identified in the US. In the specific context of the United States where there is more research, it seems that exposure to like-minded political content can potentially polarise people or strengthen the attitudes of people with existing partisan attitudes and that cross- cutting exposure can potentially do the same for political partisans.

Public discussions around science online may exhibit some of the same dynamics as those observed around politics and in news and media use broadly, but fundamentally there is at this stage limited empirical research on the possible existence, size, and drivers of echo chambers in public discussions around science. More broadly, existing research on science communication, mainly from the United States, documents the important role of self-selection, elite cues, and small, highly active communities with strong views in shaping these debates and highlights the role especially political elites play in shaping both news coverage and public opinion on these issues.

In summary, the work reviewed here suggests echo chambers are much less widespread than is commonly assumed, finds no support for the filter bubble hypothesis and offers a very mixed picture on polarisation and the role of news and media use in contributing to polarisation.

Introduction ↑

In this literature review, we examine evidence concerning the existence, causes, and effect of online echo chambers and consider what related research can tell us about scientific discussions online and how they might shape public understanding of science and the role of science in society.

We discuss online echo chambers in the context of a set of related concerns around the possible links between the rise of the internet and various digital platforms (search engines, social media, messaging applications, news aggregators, etc.) and polarisation in our societies.

Much of the existing research is focused on the United States, which is in many ways an extreme outlier among high income democracies, as political elites, the media system, and public opinion is more polarised there than in otherwise similar countries.

Thus, while we consider findings from the United States, these insights are not universally applicable. We therefore pay particular attention to comparative studies that can help capture differences and similarities across various national contexts and the situation in the United Kingdom specifically.

We seek to identify (a) areas where we believe there is a clear majority view in academic research, (b) areas where there are some empirical studies but not necessarily convergent interpretations, and (c) areas where there is at this point little evidence to help us understand a situation that is rapidly evolving in terms of both media structure and media substance (as the constant evolution of the digital media environment as well as communications around the coronavirus pandemic has shown).

Research in this area is extensive in some respects, almost non-existent in others. To avoid an overlong review, we focus our efforts on recent studies primarily in the social sciences that have a direct bearing on the possible links between media use and how the public understands the world around them. Most of the work we summarise has been peer reviewed. To be able to present an up-to-date review including work done during the coronavirus pandemic, we also draw on some empirical reports and preprints from academics working at universities and with established social science methods.

In the literature review we aim to summarise relevant empirical research and clarify the meaning of terms that are used both in public and policy debate and in more specialised scientific research, and not always in the same way. Terms like "echo chamber" and "filter bubble" have exploded into public discourse in recent years and the Hansard official record shows how these terms are increasingly used by elected officials in UK Parliamentary debates. Needless to say, public and political use of these terms are not always aligned with, or based on, scientific work. And even among academic researchers, there is not always a clear consensus on exact definitions of these concepts.

Some of the issues we discuss - around echo chambers, polarisation, and inequality - for example, raise moral and political questions and sometimes capture serious societal challenges. Our purpose here is not to outline normative positions on these but to summarise the relevant evidence. This is important to keep in mind because analytical terms such as echo chambers and polarisation often have a negative ring, but their implications of course depend on the substantive nature of the information echoed or the issues that polarise opinion.

Basic descriptive facts about media use ↑

For context, it is important to recognise some basic, often overlooked, descriptive features of contemporary media use.

First, in high income democracies, we live in increasingly digital, mobile, and platform-dominated media environments. Digital media use accounts for more than half of time spent with media, mobile accounts for more than half of time spent with digital media, and the most popular products offered by a few large US-based platform companies, most importantly Google (through e.g., Google Search and YouTube) and Facebook (through the eponymous social network, Instagram, and WhatsApp) account for a large share of time spent with digital media (Hindman 2018; Nielsen and Fletcher 2020).

Second, there is an abundant supply of news online, but on average, people spend a limited amount of time with it and many internet users do not regularly actively seek out online news. Estimates based on web tracking - where representative panels of internet users voluntarily agree to have their internet use passively recorded - show that the share of time spent online with UK news media ranges from about three percent to about six percent (see e.g., Fletcher et al. 2020c; Hindman 2018). In addition, evidence from survey data - where representative samples are asked to describe their media use - shows that just half of UK internet users in 2021 reported they had gone directly to a news website or app (e.g., BBC News, Guardian, Mail Online, HuffPost) in the past week, with the rest relying on offline sources and/or news accessed via platforms such as search or social media (Newman et al. 2021).

Third, given the ease of accessing news online and the abundant supply, differences in individuals' active choices and regular habits play a defining role in the overall distribution of news use, tending towards greater inequalities, with a large minority of news lovers, about 22% of UK internet users, engaging with many different news sources on a regular basis across many different offline and online platforms, a majority of daily briefers (55%) who use a few different sources of news and a large minority of more casual users (23%) who often do not access news daily. Differences in news use are partially aligned with differences in age, gender, education, and income, both in general (Kalogeropoulos and Nielsen 2018) and around, for example, coronavirus information (Fletcher et al. 2020b).

These descriptive features are documented through a combination of surveys relying on nationally representative samples (e.g., Newman et al. 2021) and passive tracking of large parts of the online behaviour of large samples of internet users (e.g., Fletcher et al. 2021a). Though many studies are based on quantitative data from nationally representative samples, it is important to recognise that (a) there is no single-source ground truth that captures all media use (online tracking data struggles to capture behaviour inside apps such as Facebook, does not capture offline use, and can have a hard time separating meaningful from superficial engagement), (b) surveys in particular are dependent on respondents accurately remembering and describing their media use (something many people struggle to do), and (c) news and information involve an irreducibly subjective component, as there is no objective standard for what does or does not constitute, for example, news as opposed to opinion or impartiality as opposed to partisan news, and sometimes no broad-based inter-subjective consensus either, complicating measurements. (Fox News is the single most widely used source of news in the United States, and relatively highly trusted by many on the political right - at the same time, research reported in, for example, the New York Times and Wired have treated it as if everything it publishes is misinformation or "fake news".)

Echo chambers, filter bubbles, and polarisation↑

Social scientists use the term echo chamber to describe a particular situation some people are in as a result of media supply, distribution, and/or their own demand - namely one where they occupy what Jamieson and Capella in their influential book Echo Chamber defined as "a bounded, enclosed media space that has the potential to both magnify the messages delivered within it and insulate them from rebuttal" (2008, p. 76). The magnification part is typically taken to be a preponderance of attitude-consistent information (e.g., people on the left seeking out information that reinforces their pre-existing views) and the insulation part about the absence of cross-cutting exposure (e.g., people on the right not coming across centrist or left- wing perspectives that challenge their pre-existing views).

Because echo chambers are about the media space a given individual or a group occupies, echo chambers necessarily cannot be identified by analysing behaviour on or data from a single platform. In the UK, one might read exclusively partisan newspapers, which might magnify some messages, while also watching the BBC or ITV, which would mean these messages would be unlikely to be insulated from rebuttal. Or one might primarily engage with a very partisan community on Twitter, which might magnify some messages, while also coming across news from Sky News and the local newspaper on Facebook, meaning that these messages were unlikely to be insulated. To establish whether people are truly inside echo chambers - enclosed media spaces - we have to consider all the different media and sources of news they rely on, offline and online, and across different online means of discovery (direct access, social, search, etc.).

In principle, echo chambers could concern any topic and could magnify any messages one can think of - ambiguous, benign, or malign; widely accepted or controversial; evidence-based or demonstrably false, and anything in between. In practice, social scientists have primarily researched one specific type of echo chamber, namely politically partisan news echo chambers where some people exclusively get news and information from sources that are very clearly on one side of the political spectrum.

In public and policy debate the term echo chamber is sometimes used interchangeably with the term filter bubble, but it is important to distinguish between the two.

The term filter bubble was coined by the activist and entrepreneur Eli Pariser in his book of the same name, to capture his concern that the increasing use of personalisation in the ranking of search engine results and social media feeds would create "a unique universe of information for each of us" (2011, p. 10) eroding the possibility of a relatively shared common ground - as we might be shown more and more of things we like, while things we are not prone to like are hidden from us - on the basis of data-driven display decisions dictated by platform companies' commercial interests rather than our own active choices.

The difference between the two terms is important. An echo chamber is a form of bubble, but the term does not prejudge why some people might live in such bubbles - it is possible, for example, that some actively chose to, that the situation is a result of demand more than distribution or supply. A filter bubble, on the other hand, is an echo chamber primarily produced by ranking algorithms engaged in passive personalisation without any active choice on our part, a possible outcome of specific aspects of how news and information is distributed online.

Thus, there are distinct questions of outcomes (how many people live in echo chambers versus more diverse media spaces?) and contributing causes (what is the relative importance of active users' choices versus algorithmic filtering in determining the diversity of sources people access?). Supply, distribution, and demand can all contribute to the formation of echo chambers.

Commentators and analysts typically worry about echo chambers and filter bubbles because they fear they will fuel polarisation, diminish mutual understanding, and ultimately lead to a situation where people are so far apart that they have no common ground - effectively inhabiting different realities. Polarisation can take substantially different forms. The most important forms for the purposes of this review are the following. First, ideological polarisation, which refers to the degree to which people disagree about political issues. Second, affective polarisation, which refers to people's feelings about the 'other side' - those they disagree with on a given issue. Third, news audience polarisation, which refers to the degree to which audiences for news outlets in a given country are generally more politically partisan or politically mixed.

Beyond this, the classical focus of much political science research is elite polarisation (which in turn can have very significant consequences for other forms of polarisation, an issue we return to below), and it is important to remember that while news and media use may contribute to relative increases or decreases in polarisation, many other factors are often seen by social scientists as more important - including the role of political parties in providing partisans with various cues to navigate public issues, as well as degrees of social homophily shaped by changing patterns in how and where we live, including how diverse our primary social groups are (Guess et al. 2018; Tucker et al. 2018; McPherson et al. 2001; Mason 2015).

Evidence of the existence and prevalence of echo chambers ↑

Social scientists have primarily relied on surveys, passive tracking data, and social media data to analyse the existence and prevalence of echo chambers. Of these sources of data, only surveys and tracking data can give a broader sense of what media space people occupy, as findings based on data from a single social media platform - virtually never used in isolation - cannot establish whether people inhabit a bounded, enclosed media space that magnifies messages while insulating them from rebuttal. Twitter data, for example, is often used for analysis because it is easier to access but is necessarily limited to Twitter specifically and says nothing about individuals' wider media use, let alone anything about the large majority of the population that does not use Twitter. In the UK, just 31% say that they use Twitter, with only around half of these saying they use it for news (Newman et al. 2021).

Across a range of different countries, including the highly polarised United States, several cross-platform studies - both those reliant on survey data and those reliant on passive tracking data - have found that few people occupy politically partisan online news echo chambers.

One recent study (Fletcher et al. 2021b), that includes the UK, used survey data from 2020 to assess the number of people in politically partisan online news echo chambers in Austria, Denmark, Germany, Norway, Spain, the UK, and the US by looking at how many people only use news sources with left- or right-leaning slants (measured in terms of the overall ideological slant of each outlet's audience).

In the UK, the proportion of people estimated to be in a left-leaning echo chamber is around 2% and the proportion in a right-leaning echo chamber is around 5% (Fletcher et al. 2021b). This is slightly lower than in most of the other countries covered in the study. In most other cases, a minority of around 5% of people only use news sources with ideological slants in one direction. The US is the main outlier among the seven and the only one where more than 10% of the respondents are estimated to rely only on partisan news sources. In every country covered by this study, many more internet users consume no online news at all on a regular basis than inhabit politically partisan echo chambers.

The UK results from this study are broadly similar to a previous analysis, also based on survey data, that, using a more indirect measure of diversity of news use, found that around 10% in the UK said they almost never see political content on social media that they disagree with (Dubois and Blank 2018).

These findings are consistent with several other studies of other European countries. In Sweden, for instance, Dahlgren et al. (2019, p. 170) found that while some people did engage in selective exposure to partisan news sources, rates were low overall, "suggesting a pattern of cross-cutting exposure more than isolated echo chambers." The authors note that "citizens who are frequent users of online news from one side of the ideological spectrum also tend to be more frequent users of news from the other side" (p. 170). Similarly, in Spain, Masip et al. (2020) did not find strong evidence for widespread news echo chambers and observed that most people accessed "non-like-minded media" at least sometimes. In the Netherlands, Bos et al. (2016) found some evidence of partisan selective exposure to news but noted that the formation of echo chambers was largely undercut by people's common use of relatively impartial public service broadcasting. This is also an important factor in the UK, where the BBC News website is by far the most widely used online news source (Fletcher et al. 2021b).

Even in the United States, researchers have long found that echo chambers are smaller and less prevalent than commonly assumed. Gentzkow and Shapiro (2011, p. 1831) observe that "internet news consumers with homogeneous news diets are rare," and Garrett (2013, p. 248) similarly argues that the notion that large numbers of people are cocooned in pure ideological news echo chambers, cut off from other points of view, is exaggerated and wrong.

Studies based on passive tracking that automatically log people's behaviour on one or more platforms have similar findings to analyses of survey data from nationally representative samples, though there are fewer such studies from outside the United States.

In the UK, Fletcher et al. (2020c) find a relative dearth of partisan online news echo chambers in the UK, using web tracking data collected during the 2019 General Election and show that the proportion of people in like-minded echo chambers in the UK during the election was 2% among Labour voters and 4% among Conservative voters - very similar to the results of survey-based work in the UK cited above.

Similarly, in Israel, Dvir-Gvirsman et al. (2016), using web tracking data collected around the time of the 2013 election, estimate that 3% of people were in an entirely one-sided partisan media echo chamber and that, in most cases, people in Israel had either relatively diverse media diets or did not consume online news at all.

In addition to people's common use of relatively impartial public service broadcasting undercutting the existence of partisan echo chambers, we should keep in mind that - at least online - their potential size is limited by the fact that many people do not consume much online news in the first place. In the UK, around 25% of internet users say they access no online news at all each week (Newman et al. 2021).

Related research - often not specifically aiming to measure the size of echo chambers - often arrives at broadly similar conclusions by analysing patterns of media use. Again, even in the polarised United States, the results are largely similar. Using network analysis and combining TV and internet tracking data, Webster and Ksiazek (2012) find high degrees of audience overlap across news sources and concentration of audiences on large mainstream outlets. Guess (2016, pp. 17-18) observes, based on analysis of tracking data, that there is a "remarkable degree of balance in respondents' overall media diets regardless of partisan affiliation.

Whether Democrat, Republican, or independent, the large bulk of these individuals' media diets cluster around the center of the ideological spectrum." Similarly, Nelson and Webster (2017) find that audiences are concentrated on a few popular political news sites and that, in general, political news sites, irrespective of popularity, have ideologically diverse audiences.

Yang et al. (2020), working with desktop and mobile data from Comscore's panels, also observe that ideologically diverse US audiences converge on mainstream news outlets online, find little evidence of ideological selective exposure and, contrary to what some have suggested, find increasing co-exposure to news sources over time. Reinforcing the results from survey data, the authors also note that many more internet users consume no online news at all than rely solely on partisan sources.

Single-platform studies are, as noted, of limited value in identifying echo chambers, but there are several important studies that identify like-minded communities formed on individual social media platforms - whether through algorithmic selection, self-selection, or some combination thereof (Bakshy et al. 2015; Barberá et al. 2015; Kaiser and Rauchfleisch 2020; Vaccari et al. 2016). Even these, however, often conclude, like Barberá (2015, p. 28), that "most social media users receive information from a diversity of viewpoints." And in the absence of evidence on what other media the individuals involved use in addition to the social media platform in question, these studies simply cannot establish whether people inhabit a bounded, enclosed media space where specific messages are magnified and insulated from rebuttal.

In summary, studies in the UK and several other countries, including the highly polarised US, have found very similar results whether relying on survey data or passive tracking data. Most people have diverse media diets, those who rely on only one source typically converge on large sources with politically diverse audiences such as commercial or public service broadcasters, and only small minorities, often only a few percent, exclusively get news from partisan sources.

Who might end up in echo chambers and why? ↑

Though a large number of empirical studies from different countries and relying on various kinds of data find that echo chambers are smaller and less prevalent than often assumed, it is still important to consider who might end up in echo chambers and why.

To understand what news and information people see, media researchers typically seek to consider the interplay between media supply, media distribution, and media demand(Webster 2014).

While the term "echo chamber" has sometimes been used to describe the news institution as a whole as "the media echo chamber" (Bennett et al. 2007), in a liberal democracy with a diverse media system and a multitude of independent voices expressed online, it is hard to see how, on most issues of importance, supply alone could lead to the formation of echo chambers. Information supply has grown enormously in recent years thanks to the growth of the internet. The data and analytics company Comscore tracks more than one thousand news and information providers online in the UK alone, and that is without counting the variety of information people can access from activists, authorities, educational institutions, political parties, scientists, and many, many other sources available online.

This leaves distribution and demand as the main possible causes for the formation of echo chambers.

In terms of distribution, algorithmic selection by digital platforms such as search engines and social media that make personalised display decisions for countless users using automated systems might, some fear, generate filter bubbles by reducing the diversity of information people come across, serving them more attitude- consistent news and resulting in less cross-cutting exposure.

But empirical studies, whether based on survey data or passive tracking data, have generally found the opposite. They demonstrate that reliance on secondary gatekeepers such as search engines and social media - whatever other problems might be associated with them - is in most cases associated with more diverse news use.

This is a consistent finding across a growing number of survey-based studies (DuBois and Blank 2018; Fletcher and Nielsen 2018a; Fletcher and Nielsen 2018b; Beam et al. 2018) and studies based on various forms of passive tracking data (Flaxman et al. 2016; Cardenal et al. 2019; Fletcher et al. 2021a; Sharkow et al. 2020; Wojcieszak et al. 2021). These findings also hold across different countries (including the United Kingdom and the United States), across different digital platforms (search engines, different social media platforms, and news aggregators), and across different methods and modes of analysis. These avenues particularly increase exposure for people who are less likely to otherwise visit news sites directly (Fletcher and Nielsen 2018a; Wojcieszak et al. 2021). We are not aware of any comparable studies that have found support for the filter bubble hypothesis that algorithmic ranking leads to echo chambers (see Bruns 2019 for a more detailed overview).

To understand why algorithmic selection is consistently found to lead to more diverse news diets, not narrower diets (let alone echo chambers), it is important to remember that the median number of different sources of news that people in the UK use on a weekly basis offline is two, and just one online (Newman et al. 2021). Search engines and social media do not vastly expand this number and it is not the case that people who use these platforms have very diverse and balanced news diets. Rather, they lead people to slightly more, and slightly more diverse, sources of news than what they seek out of their own volition.

The two main drivers of this are automated serendipity, where ranking algorithms may return a result in, for example, a search query from a source that people might not normally access directly (Fletcher and Nielsen 2018b), and incidental exposure, where people come across and read news articles while on, for example, a social media site they primarily use for other purposes (Fletcher and Nielsen 2018a). (Both effects are broadly aligned with the commercial self-interest of platform companies - automated serendipity creates an experience of variety, and incidental exposure can increase time spent on social media as people come across the occasional interesting article.)

These effects are not equally strong for everyone. Passive personalisation based on past behaviour may well make algorithms more likely to recommend more news to those who already engage with a lot of news (Thorson 2020), but they are particularly important - and arguably more beneficial - for those least likely to actively seek out a lot of news on their own, such as younger people and those with lower interest in news (Fletcher and Nielsen 2018a; Wojcieszak et al. 2021).

The role of interest leads us to demand, the final factor we will consider in this section. The main possible causal mechanism here is self-selection: that some people actively opt into echo chambers because they prefer news that aligns with and reinforces their pre-existing views (selective exposure to attitude-consistent information) or actively seek to avoid counter-attitudinal information (selective avoidance of cross-cutting exposure).

Research on self-selection is predominantly a mix of survey and passive tracking studies akin to those discussed above, which often have high external validity but can have limited internal validity, and experimental studies that have higher internal validity but often limited external validity because of the difficulties of creating a situation akin to what media users face outside the experiment.

Broadly, a number of studies find that while many people do engage in some degree of selective exposure, they do not necessarily engage in selective avoidance (Bos et al. 2016; Garrett 2009; 2013; Garrett and Stroud 2014; Jang 2014; Johnson et al.

2011; Kim and Lu 2020; Trilling and Schoenbach 2015). As Weeks et al. (2016, p. 263) write of the US context, "although partisans exhibit some preference for like- minded sources, we find no evidence that they avoid disagreeable information but rather continue to rely mostly on a common set of mainstream, general interest news outlets. These more mainstream sources provide information that at times challenges both Republicans' and Democrats' positions, yet neither make an attempt to avoid them."

This means, for example, that while those with highly partisan political views are significantly more likely to use partisan news media with a similar orientation, they are not necessarily less likely to use other news media with a different orientation.

Evidence from selective exposure experiments - which show different people's tendencies to select content they agree or disagree with - has indicated that the people most likely to choose pro-attitudinal content are those who have stronger partisan/ideological beliefs, are more certain about their beliefs, or who are more politically interested/engaged (Iyengar and Hahn 2009; Knobloch-Westerwick and Meng 2009; Kim and Lu 2020). Similarly, studies focused on specific social media also find that people most likely to be deep into like-minded communities are strong partisans (Barberá 2015; Boutyline and Willer 2017).

But, importantly, many people do not have particularly strongly held political views and do not primarily approach news and media through a political lens (Bos et al. 2016; Yang et al. 2020). Often, basic interest, in turn partly aligned with levels of education and income, is a more important factor.

In Europe, public service media often help bridge these gaps, with differences between those with low interest and high interest being smaller in countries like the UK that have widely used public service media (Castro-Herrero et al. 2018). But many people self-select away from politics and news, choosing entertainment content instead (Prior 2005), and people with more limited levels of formal education and lower levels of income generally use less news than more privileged parts of the population (Kalogeropoulos and Nielsen 2018).

This is a reminder that, while of great interest to politicians and political scientists, politics is not the only, or even main determinant of news and media use. Media use is often more shaped by habits that in turn reflect a mix of instrumental uses and preferences anchored in social contexts that often exhibit more significant homophily in terms of social class than in terms of politics (Bos et al. 2016; Dahlgren 2019; Dubois and Blank 2018; Garrett et al. 2013; Johnson et al. 2011; Skovsgaard et al. 2016; Van Aelst et al. 2017; Yang et al. 2020).

In summary, studies in the UK and several other countries, have found that algorithmic selection generally leads to slightly more diverse news use - the opposite of what the filter bubble hypothesis posits - but that self-selection, primarily among a small minority of highly partisan individuals, can lead people to opt in to echo chambers, even as the vast majority do not, and document that limited news use remains far more prevalent than echo chambers are.

Media use and polarisation ↑

Polarisation, in social science, refers to divisions between groups. It can be used to describe a situation where divisions are already sufficiently large to be considered polarised, or a process whereby divisions are becoming larger over time (even though they may still be quite small). Polarisation can take many forms and is not always intrinsically problematic (some things are worth disagreeing over, see Kreiss 2019).

Just as many assume that echo chambers are pervasive and filter bubbles are real - despite evidence to the contrary - there is widespread public and political concern over polarisation in many countries. Some surveys which aim to measure perceived polarisation suggest much of the public feel that the UK is more divided today than in the past.

Social scientists examine a range of different kinds of polarisation of the public, including ideological polarisation, affective polarisation, and news audience polarisation. As with any attempt to measure a change over time, decisions about what time frame to consider will influence conclusions, and often, there is little consistent data allowing for rigorous longitudinal analysis.

Ideological polarisation, sometimes termed issue polarisation, has been a long- standing focus of political science and focuses on divisions in public opinion on a range of different policy issues. Looking back several decades, comparative research based on the World Values Survey has found some support for the idea that Left/Right polarisation has decreased in many high-income democracies and electoral volatility increased, even as other divisions, sometimes over so-called "post-material" issues, have become more important (Dalton 2006; Ingelhart 2017). However, divisive elections in the last two decades have led to a resurgence of interest in whether political partisans' views have become more polarised again (Abramowitz 2010; Fiorina and Abrams 2008; Iyengar et al. 2012), with one summary in the United States concluding that while public opinion overall on many issues has not become markedly more polarised in recent years on average, it has increased among the most politically engaged (Prior 2013; see also Mason 2013). In the UK, in the longer term, the polarisation of party supporters has declined since the 1970s, as have both perceived polarisation and expert evaluations of polarisation (Rehm and Reilly 2010). Other analyses have also found declines in polarisation in the UK (Adams et al. 2012a; Adams et al. 2012b), but many of these studies are from before Brexit and other recent divisive political discussions.

Affective polarisation refers to how much opposing partisans dislike one another. Most research on affective polarisation has been conducted in the United States and, in contrast to ideological polarisation, affective polarisation clearly seems to be on the rise - as one team of researchers find that ordinary Americans increasingly dislike and distrust those from the other party (Iyengar et al. 2019; see also Mason 2013). In the UK, there is evidence that affective polarisation exists between Labour and Conservative voters and also around opinion-based groups that either support or oppose Brexit (Hobolt et al. 2021). Comparative work on affective polarisation is in its infancy, but a few studies have been published recently. They find that levels of affective polarisation vary greatly by country (complicating the notion that polarisation is pronounced everywhere) and document considerable variation in patterns over time (belying the notion that a single universal cause - for example the spread of the internet - is driving polarisation everywhere) (Gidron et al. 2019; Boxell et al. 2020; Reiljan 2020). In several of these studies, Britain is found to have higher levels of affective polarisation than multiparty political systems in other parts of Northern and Western Europe, though one of these studies actually suggests affective polarisation in the UK may have declined since the 1980s (Boxell et al. 2020).

News audience polarisation, finally, refers to the structure of aggregate public attention to news media, to whether a country is home to large news outlets with both strongly left- and strongly right-leaning audiences, as opposed to outlets with mostly mixed or centrist audiences (Fletcher et al. 2020a). Again, the situation in the United States - where highly partisan news brands including, most importantly, Fox News play an important role - has led some to suggest that news media use is increasingly fragmented and polarised. Despite the very real importance of some highly partisan brands (Benkler et al. 2018), even in the United States, Nelson and Webster's (2017) analysis of Comscore tracking data found that, despite huge choice, most people tend to congregate around a few popular news outlets and that all news sites - including partisan ones - attract reasonably ideologically diverse audiences. Comparative work based on survey data from a sample of 12 high-income democracies suggests that cross-platform (online and offline) news audience polarisation is highest in the United States but lower within Europe. The UK has relatively high levels of news audience polarisation by European standards - largely thanks to widely used partisan newspapers - but overall levels are still low (Fletcher et al. 2020a).

There is an extensive literature in political science and sociology about the drivers of polarisation, with considerable attention to elite cues from politicians (Rogowski and Sutherland 2016; Iyengar et al. 2012) and to social dynamics including social homophily and various kinds of social sorting - all predominantly rooted in our offline lives, where we work, who we spend time with, where we live (see e.g., Guess et al. 2018; McPherson et al. 2001; Mason 2015).

There is also some work on media, including media reporting about polarisation. US studies find that exposure to like-minded partisan media under experimental conditions can strengthen the views of already partisan individuals (Levendusky 2013). Panel survey work, which measures the same people's media use and attitudes at different points in time, has also found that using like-minded partisan media in the US can increase anger toward the 'other side' and make people more willing to share political information on social media (Hasell and Weeks 2016). At the same time, cross-cutting exposure, at least on social media, also seems to be able to increase polarisation, at least among political partisans (Bail et al. 2018).

Furthermore, experimental work finds that exposing people to media coverage about political polarisation may in itself increase perceptions of polarisation and contribute to increased dislike for the opposition (Levendusky and Malhotra 2016).

Work in the United States stresses that these factors are sometimes asymmetrical, in terms of the degree to which individual political leaders actively seek to polarise the public, and in terms of the role of individual news media - there is a growing literature documenting the impact of Fox News specifically (DellaVigna and Kaplan 2007; Hopkins and Ladd 2013). Furthermore, broader changes in both news supply and media use, suggesting a relative decline in coverage of and attention to local politics and more focus on often more divisive national politics, also seems to be a possible contributor to polarisation in the United States (Martin and McCrain 2019).

There is much less work from outside the United States and no clear overall set of convergent findings. Experimental work from the Netherlands, for example, has found that while people might have a tendency to engage in selective exposure, this does not necessarily polarise people's attitudes (Trilling et al. 2017). Yet Wojcieszak et al. (2018), analysing panel survey data in the Netherlands, found that people with strong opinions about the EU polarised in their views after being exposed to news about the EU.

In summary, the picture on polarisation is complex and research is often limited outside the United States. Overall, ideological polarisation has, in the long run, declined in many countries, but affective polarisation has in some cases, but not all, increased. News audience polarisation is much lower in most European countries, including the United Kingdom. Much depends on the specifics of individual countries and what point in time one measures change from, and there are no universal patterns, suggesting country-specific factors drive national developments, including most importantly the behaviour of political elites and social dynamics.

When it comes to media, there is limited research outside the US and this work does not always find the same patterns as those identified in the US but, at least in the specific context of the United States, it seems that exposure to like-minded political content can potentially polarise people or strengthen the attitudes of people with existing partisan attitudes, and that cross-cutting exposure can potentially do the same for political partisans.

Digital media and public discussions around science ↑

Virtually all the studies reviewed above on echo chambers, filter bubbles, and polarisation are concerned with the interplay between politics, news, and media use.

This work provides a set of basic findings about the dynamics of contemporary news media use, the relative importance of algorithmic ranking versus self-selection in shaping it, and how engagement with some forms of information can further strengthen already strongly held beliefs.

Many of these dynamics may also be relevant for understanding the role that digital media play in public discussions around science topics. But research in this area is largely separate from the political communication and media research traditions discussed above that have usually focused on politics specifically, or news and media use as a whole, rather than specific topics. And while terms like echo chambers, filter bubbles, and the like are beginning to feature in discussions among scientists and science communication scholars, these discussions, when research- based, are generally based on the research reviewed above (see e.g., Scheufele et al. 2017; Scheufele and Krause 2019; West and Bergstrom 2021). Empirical research specifically on the possibility of echo chambers and the like in online scientific discussions is still very limited.

There are different strengths and weaknesses of either looking at media use as a whole or focusing on discussions around different topics - and this can sometimes lead to different results and interpretations. Scholarship on selective exposure and polarisation around various science issues illustrates how the concepts, theories, and methods the research reviewed so far relies on can also shed light on science discussions but also underlines that dynamics are not always the same with these topics as with, say, party politics.

In the US, some studies have examined selective exposure around various science issues like genetically modified foods, nanotechnologies, stem cell research, and fracking (Jang 2013; Knobloch-Westerwick et al. 2015; Yeo et al. 2015). These studies have found evidence of selective exposure for some topics but, in general, the preference for like-minded information is less clear than it is for partisan political topics, it varies by subject, and sometimes shows opposite patterns in which people seek out opposing views (see Stroud 2017 for a review). Indeed, in the US, "not all science topics cohere with partisan perspectives" (Stroud 2017, p. 379) and it is likely that the strength and direction of these associations varies not only by topic but also from one country to the next.

That said, there are growing concerns about selective exposure in scientific domains that have become politically aligned, emotionally loaded, and more explicitly and actively politically polarised in some countries (e.g., Nisbet et al. 2015).

Elite cues seem critically important here. In making sense of how climate change became a more ideologically polarised issue in the United States over time, even as the scientific consensus grew stronger, Merkley and Stecula (2018) analysed news items over more than three decades and found that while Democratic politicians consistently acknowledged climate change in the media over time, Republican messages were ambiguous - and polarisation increased as more Republican-elected officials began actively and publicly denying climate change. They suggest that Republican voters' rejection of climate science may have been in part a direct response in opposition to cues from Democratic elites these voters regard with scepticism, a proposition they provide further evidence for in a separate study based on more than 200 surveys between 1984 and 2014 (Merkeley and Stecula 2020).

Similarly, Brulle et al. (2012, p. 185) argue "the most important factor in influencing public opinion on climate change [is] the elite partisan battle over the issue," and they show how US public opinion on the threat of climate change has been moved more by elite cues - in particular, Republican politicians' opposition to climate change bills - than by media coverage, which by and large mirrored those cues while also presenting the Democrats' position.

Analogous arguments have been made in light of the COVID-19 pandemic, where research in the US has shown polarisation in elite communication about the issue, with Democrats emphasising threats to public health and American workers, and Republicans placing greater emphasis on China and businesses early on in the pandemic (Green et al. 2020; see also Hart et al. 2020). Hamilton and Safford (2021) used survey data to show that trust in science agencies like the CDC declined rapidly among Republicans in the US but not Democrats, following Donald Trump's changing views toward the CDC, views that were in turn amplified by conservative media including Fox News. This further underlines how top-down cues from political elites, including most prominently the President, were crucial to the deep partisan divide that formed around the subject.

The relative absence of similar levels of polarisation in discussions around and public opinion on issues such as climate change and the coronavirus in many countries where political elites have behaved differently is a reminder of how important country-specific factors are, even as wider changes in the media system may be broadly similar. Content-analysis based research has in the past shown how, for example, news coverage of climate change is broadly based on the scientific consensus in countries where most elected officials recognise it but features climate change deniers and their views more prominently in countries where some politicians or even whole political parties have embraced these views (Painter and Ashe 2012). Work from Canada (Merkley et al. 2020) demonstrates the importance of cross-party consensus elite cues in shaping the public's perception of and response to COVID-19, just as work from Brazil (Gramacho et al. 2021) shows how very differently things developed in Brazil as Jair Bolsonaro's most avid supporters took their cues from the President.

A growing body of research has also sought to better understand the dynamics of public discussions around science online. Much of this work is not concerned with echo chambers, filter bubbles, and polarisation as defined here, but it can help us understand some of the ways in which digital media are used to discuss science.

In general, much of this research has documented a tendency for social media groups that actively discuss scientific issues to be made up of somewhat homogenous, segregated, and often polarised communities of interest with limited intersections between groups holding opposing views, and the vast majority of users on any given platform not taking any active role in any of these discussions of science. This is similar to broader dynamics online where communities of interest tend to coalesce, often around perfectly benign shared interests but sometimes around more ambiguous activities and, of course, in some instances, around shared interest in views that are demonstrably false and activities that are potentially harmful (Philips and Milner 2017).

Some of this research has compared differences between groups that form around science versus conspiracy news (Del Vicario et al. 2016a; Del Vicario et al. 2016b), whereas other research has focused on specific topics like vaccines (Cinelli et al. 2021; Cossard et al. 2020; Dunn et al. 2015; Schmidt et al. 2018), abortion (Cinelli et al. 2021), climate change (Williams et al. 2015), and most recently COVID-19 (Wang and Qian 2021).

In line with studies of political discussions online, people identified as active in these online discussions are often relatively small minorities who have strong attitudes on the subject (Williams et al. 2015), potentially skewing the online debate toward the more extreme attitudes of these active, vocal, opinionated minorities. In their analysis of the Italian vaccination debate on Twitter, Cossard et al. (2020) found differences in the structures of the polarised groups. Vaccine sceptics tended to form smaller but louder groups that were more tightly connected, whereas vaccine advocates tended to organise in a more hierarchical fashion around certain authoritative actors. Elsewhere, scientists have studied alternative media ecosystems involving websites and various other actors who repeatedly share the same disinformation, often across a wide variety of issues and topics, advancing political conspiracies alongside content such as climate science denial, anti-vaccine pseudoscience, and flat earth theory and trying to promote these on social media platforms such as Twitter to reach beyond those already actively involved in the communities in question (Starbird 2017; Starbird et al. 2019).

These studies are important and valuable for understanding online social dynamics and well-suited to provide evidence for self-selection, homophily, and polarisation dynamics between groups that focus on controversial topics (and how they may vary from one platform to another), but they are less helpful in examining the phenomenon of echo chambers as understood here - despite the occasional use of the term "echo chambers" in a broader sense of people being actively engaged in an online community of like-minded people. Because many of these are single- platform studies based on data from one social network, and often deliberately study already highly engaged and therefore unusual communities (rather than nationally representative samples or exhaustive studies of all users), we should be cautious about (a) generalising to the population at large, (b) generalising to discussion of other less divisive topics, and (c) whether they accurately describe the vast majority of social media users who are not engaged in these discussions, or have only the most peripheral contact with them.

In summary, public discussions around science online may exhibit some of the same dynamics as those observed around politics - with an important role for self- selection, elite cues, and small highly active communities with strong views in explaining the dynamics of these debates - but fundamentally, while important studies have been published, mostly from the US, there is at this stage limited empirical research on the possible existence, size, and drivers of echo chambers in public discussions around science. More broadly, years of research document the role political elites play in shaping both news coverage and public opinion around science issues as well.

Conclusion ↑

In this review, we have examined evidence concerning the existence, causes, and effects of online echo chambers and have considered what related research can tell us about scientific discussions online and how they might shape public understanding of science and the role of science in society.

There are a number of areas where our review suggests that there is a clear majority view in academic research, including most notably:

Politically partisan online news echo chambers are generally small - much smaller than is often assumed in public and policy debate.

Automated serendipity and incidental exposure mean that relying on search engines, social media, and other digital platforms using algorithmic ranking leads people to slightly more diverse news - the opposite of what the filter bubble hypothesis posits.

Self-selection, both along partisan lines and, importantly, in terms of levels of interest, plays a significant role in shaping news and media use.

There is no single uniform trend towards greater polarisation - ideological polarisation has declined in some countries, affective polarisation has increased, news audience polarisation varies greatly, and in every case, country-specific factors seem decisive, not a single global trend like the rise of the internet.

Elite cues continue to play an important role in shaping both news coverage and public opinion, with the behaviour of political parties and individual prominent politicians often contributing to polarisation, whether around ideological issues or science issues such as climate change.

Given the amount of research on these issues arriving at broadly similar conclusions, the burden of proof now, in our view, is on those arguing for alternative interpretations to provide systematic evidence.

Furthermore, there are several areas where there are some empirical studies but not necessarily convergent interpretations, or only convergent interpretations supported by data from a single or a few countries. These areas include, most notably:

News audience polarisation is higher online than offline in some but not all countries.

Engagement with partisan news and media can increase polarisation.

Cross-cutting exposure may also increase polarisation among partisans.

Relatively small minorities of unusually active and opinionated individuals often animate online debates, even as the vast majority of internet users pay little or no attention to these partisan discussions.

There is evidence for all these, but it is either mixed or exclusively from one country, so we cannot necessarily assume these findings apply everywhere.

Finally, there are many areas where there is at this point little empirical research to help us understand a situation that is rapidly evolving. These include, among other things:

Whether echo chambers and the like work broadly in the same ways around science issues as around more conventionally political issues.

How self-selection driven by forms of opinion other than political ones operate and how self-selection based on partisanship and/or interest shapes engagement with news and information around science.

The extent to which dynamics identified in studies of online news sites/apps and established digital platforms including search engines and social media are similar on less studied but potentially important platforms including messaging applications, video sites, and new social media platforms.

In closing, let us make three final points.

First, a large number of empirical studies documenting that echo chambers are smaller than commonly assumed, and a growing amount of research rejecting the filter bubble hypothesis should not be confused with a Panglossian belief that we live in the best of all possible worlds or that our increasingly digital, mobile, and platform-dominated media environment does not come with any serious societal challenges. There are many, including the frequently overlooked fact of pronounced inequality in news and information use documented by many of the studies reviewed here, as well as a multitude of others, such as widespread online harassment and abuse, various kinds of misinformation, often invasive data collection by dominant platforms, a serious disruption of the established business of news and market concentration, and many more issues beyond the scope of this review.

Second, the risks associated with people primarily seeking out attitude-consistent information, let alone living in bounded media spaces where their pre-existing views are rarely challenged, can be much smaller than many believe while still being present, and it is clearly possible for people to come to hold very polarised views - sometimes views that are contradicted by the best available scientific research - without living in echo chambers or filter bubbles. Sometimes minorities, however small, play an important role in driving public and policy debate and decision making. (As Guess (2021, p. 12) puts it, in the US context, "even if most Americans do not exist in online echo chambers, they are subject to the political influence of those who do.") And sometimes confirmation bias, motivated reasoning, and social reinforcement from the communities we spent most of our offline lives with will mean we have very strong views, even as we also see a wide range of different kinds of information via news and media.

Third, research in this area is extensive in some respects, almost non-existent in others. Among other things, there is often a dearth of research outside the United States, much less research on scientific issues specifically than on politics and media use more generally, and little work on several important established platforms and many newer and smaller platforms - and all this work could be greatly facilitated if academic researchers had better access to data in a privacy compliant and secure way from platform companies.

Footnotes

Beyond the definitions we rely on here, the terms "echo chamber" and "filter bubble" are used in a variety of other often broad and ambiguous ways, both by academics and in public debate, and there is limited consensus on singular clear definitions.

And indeed, evidence from the US has shown that some who experience little or no cross-cutting exposure on Twitter still encounter opposing views via television news - suggesting that analysis of echo chambers based on Twitter data alone will overestimate their size (Eady et al. 2019).

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This literature review was published with the support of the Royal Society.
This report can be reproduced under the Creative Commons licence CC BY. For more information please go to this link.

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Echo chambers, filter bubbles, and polarisation: a literature review

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DOI: 10.60625/risj-etxj-7k60 Terms like echo chambers, filter bubbles, and polarisation are widely used in public and political debate but not in ways that are always aligned with, or based on, scientific work. And even among academic researchers, there is not always a clear consensus on exact definitions of these concepts.

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Most social media companies have their own content policy that explicity states what kind of content is allowed on the platform and what they will do if users post inappropriate content. This is a rapidly evolving field.
I would add, not very effective a lot of the time.
But, social media platforms (publishers)are more consumed than other traditional forms of media, with no editing or quality control, so moderation is essential

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A lesson plan on how to understand how we search and the effect of algorithms on search results, using Bakke's 2020 article.

Aims:

Understand how algorithms affect search results in general,
and increase your awareness of how your search process affects the sources you find.

Report on and critique your searches to improve balance of results, that is, practice reporting data, visually displaying data, and making recommendations based on that data.

Actions:

1. Observe and screen record your search process

2. Analyse and report on your process, using at least one visual

3. Evaluate and critique your process

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https://www.politicalcompass.org/test
https://www.allsides.com/media-bias/rate-your-bias

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If you have been directed here directly from another site, we ask that you read our homepage before continuing

Before taking the test: Please note that this isn't a survey, and these aren't questions. They're propositions. To question the logic of individual ones that irritate you is to miss the point. Some propositions are extreme, and some are moderate. That's how we can show you whether you lean towards extremism or moderation on the Compass. Your responses should not be overthought. Some of them are intentionally vague. Their purpose is to trigger reactions in the mind, measuring feelings and prejudices rather than detailed opinions on policy.

Page 1 of 6

Just a few propositions to start with, concerning - no less - how you see the country and the world.

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self-test of your position on 2 political dimensions

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Articles about 'going down the rabbit hole' and why we believe what we believe

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Articles about 'going down the rabbit hole' and why we believe what we believe

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Motivational reasoning: Kunda, Z. (1990). The case for motivated reasoning. Psychol Bull, 108(3), 480-498. doi:10.1037/0033-2909.108.3.480

Analytical thinking: Pennycook, G., & Rand, D. G. (2019). Fighting misinformation on social media using crowdsourced judgments of news source quality. Proceedings of the National Academy of Sciences, 116(7), 2521. doi:10.1073/pnas.1806781116

Echo chambers: Bessi, A. (2016). Personality Traits and Echo Chambers on Facebook. Computers in Human Behavior 65, 319–24. DOI:10.1016/j.chb.2016.08

There are many factors that influence someone's perception. The following three factors may explain why some people are more likely to go down the rabbit hole.

1. Motivational reasoning : people often engage in a reasoning process designed to maintain, rather than change, their perceptions of reality even when facing information that contradicts their initial beliefs. Thus, motived reasoning may explain the credibility and acceptance of misinformation.

2. Lack of analytical thinking : people may lack the skills to analyse the information.

3. They are already in echo chambers: some tend to join communities that share their values, which then reinforce their pre-existing beliefs.

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Go outside the website.
This is NOT close reading the site you are on! To find who is behind the information and if it is trustworthy - leave a website to find information about what other sources say about it.

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This lesson introduces students to lateral reading, a strategy for investigating who's behind an unfamiliar online source by leaving the webpage and opening a new browser tab to see what trusted websites say about the unknown source. Students watch the teacher model lateral reading and then have a chance to practice the strategy to determine who is behind a website and, ultimately, whether that website is trustworthy.

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Intro to Lateral Reading | Civic Online Reasoning

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This lesson introduces students to lateral reading, a strategy for investigating who's behind an unfamiliar online source by leaving the page and opening a new browser tab to see what trusted websites say about the unknown source.

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How to spot a deepfake video made with FakeApp

1. Don't jump to conclusions

2. Consider the source

3. Check where it is and is not online

4. Inspect the mouth

5. Slow it down

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You're looking at the future of fake news and propaganda.

Don't be embarrassed if you were fooled, even if only briefly. Technology to trick our eyes and ears is advancing rapidly. Teams in Germany are working on Face2Face, the type of face- and voice-swapping technology used to create the video above. Software giant Adobe is creating a "Photoshop for audio" that makes it easy to edit and manipulate what someone has said, as is a Montreal startup called Lyrebird. After you've selectively edited someone's words, you could take that audio and use tech developed at the University of Washington to generate a video of the very same person speaking those words, just to make it fully convincing.

The ObamaPeele video was created using another emerging technology: FakeApp. It's a free tool that's recently been used to insert the faces of celebrities into porn videos. Other people have put Nic Cage into Indiana Jones, face-swapped Nic Cage and Lois Lane, and created other cinematic crimes against humanity.

Part of the process for creating this video involved taking an original video of Barack Obama and pasting Jordan Peele 's mouth into it. It looked really bad and clumsy at first. But things got remarkably better the longer FakeApp was left to process a more believable amalgamation of Obama's head and Peele's mouth. That took roughly 56 hours and was overseen by a video effects professional.

So the good news is it still requires a decent amount of skill, processing power, and time to create a really good "deepfake."

The bad news is that the lesson of computers and technology is this stuff will get easier, cheaper, and more ubiquitous faster than you would expect - or be ready for. Even at this early stage it's proving difficult for humans to consistently separate deepfakes from real videos, according to Matthias Niessner, who runs the Visual Computing Lab at Technical University Munich and leads the research on Face2Face.

"Essentially, we have a user study where we asked people try to spot the difference - turns out we humans are not so great at it," he told BuzzFeed News.

This is why experts in computer science have been warning that an age of ubiquitous deepfakes could help usher in an "infocalypse."

Niessner, however, is hopeful that groups like his are making progress training artificial intelligence to detect a fake.

"On the positive side, we managed to train several neural networks that are indeed pretty good at figuring out forged images/videos. ... Ideally, we're imagining automated methods in a browser or social media platform to tell what's fake and what's real," he said.

But right now the technology to create effective fakes is widely available, thanks to FakeApp. The tech to spot them is not. This is where you come in.

There are basic tips you can follow to ensure you don't get fooled easily. If more people are equipped to spot a fake, then the world has a better shot at avoiding a dystopian hellscape where every movie stars Nic Cage, and governments, trolls, and companies manufacture perfect fakes that collectively make us lose touch with reality.

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How To Spot A DeepFake Like The Barack Obama-Jordan Peele Video

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This "deepfake" video starring Jordan Peele as Barack Obama shows how easy it's getting to create convincing audio and video fakes. Here's how to fight back.

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Deepfake Detection Challenge Results: An open initiative to advance 

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We're sharing results of the Deepfake Detection Challenge, an open, collaborative initiative to spur creation of innovative new technologies to detect deepfakes and manipulated media.

The competition drew more than 2,000 participants, who trained and tested their models using a unique new dataset created for the challenge.

Tested against a black box dataset with challenging real world examples that were not shared with entrants, the top model achieved an accuracy of 65.18 percent. This establishes a new shared baseline as the AI community continues to work on this difficult and important task.

We partnered with other industry leaders and academic experts last year to create the Deepfake Detection Challenge (DFDC) in order to accelerate development of new ways to detect deepfake videos. By creating and sharing a unique new dataset of more than 100,000 videos, the DFDC has enabled experts from around the world to come together, benchmark their deepfake detection models, try new approaches, and learn from each others' work. This open, collaborative effort will help the industry and society at large meet the challenge presented by deepfake technology and help everyone better assess the legitimacy of content they see online. As with our recently launched Hateful Memes Challenge , we believe challenges and shared datasets are key to faster progress in AI.

The DFDC launched last December, and 2,114 participants submitted more than 35,000 models to the competition. Now that the challenge has concluded, we are sharing details on the results and working with the winners to help them release code for the top-performing detection models. Next week, at the conference on Computer Vision and Pattern Recognition (CVPR) , we will also share details on our plans to open-source the raw dataset used to construct DFDC, featuring more than 3,500 actors and 38.5 days' worth of data. This will help AI researchers develop new generation and detection methods to advance the state of the art in this field. Moreover, this dataset will be opened for use for other research work in AI domains as well as work on deepfakes.

These examples were taken from the Deepfake Detection Challenge dataset. Can you determine which are the original, unmodified videos and which are the deepfakes? (Answers appear at the end of this blog post.)

A challenge and dataset designed expressly for research on deepfakes

To create this challenge, we built a new dataset with a wide variety of high-quality videos created expressly for research on deepfakes. DFDC participants used this dataset to train and test their models and were able to assess their performance on a public leaderboard on the challenge website. The videos feature more than 3,500 different paid actors, each of whom agreed to participate in the project. We focused in particular on ensuring diversity in gender, skin tone, ethnicity, age, and other characteristics.

We altered the videos using a variety of different deepfake generation models, refinement techniques such as image enhancement, and additional augmentations and distractors, such as blur, frame-rate modification, and overlays. Our goal was to make the dataset representative of the variety of qualities and adversarial methods that could occur in real-world videos shared online. To ensure that the challenge would address researchers' needs, we also worked with experts from Cornell Tech, MIT, Technical University of Munich, UC Berkeley, University at Albany-SUNY, University of Maryland, University of Naples Federico II, and University of Oxford to gather feedback and recommendations.

One of the central unsolved challenges of detecting deepfakes is that it is hard to generalize from known examples to unfamiliar instances. We designed the DFDC with this in mind. To determine the winners, participants in the challenge submitted their code to a black box environment. This separate dataset was not available to entrants, so they had to design models that could be effective even under unforeseen circumstances. The black box dataset consisted of 10,000 videos that were not available to participants in the competition. It contains both organic content (both deepfakes and benign clips) found on the internet and new videos created for this project. We verified that the distribution of fake and real videos was identical to that of the public test set.

We added videos of makeup tutorials, paintings, and other examples that might be difficult for detector models to classify correctly. We also randomly applied a number of augmentations to emulate how potential bad actors could modify videos to try to fool detectors. Examples include applying AR face filters, adding random images to each frame, and changing the frame rate and resolution. Augmentations were also applied to the public test set, but the black box test set used additional techniques to increase the difficulty level.

Results and impact of the DFDC

The top-performing model on the public dataset achieved 82.56 percent accuracy. But when evaluating the entrants against the black box dataset, the ranking of top-performing models changed significantly. The highest-performing entrant was a model entered by Selim Seferbekov. It achieved an accuracy of 65.18 percent against the black box dataset. Using the public dataset, this model had been ranked fourth. Similarly, the other winning models, which were second through fifth when tested against the black box environment, also ranked lower on the public leaderboard. (They were 37th, 6th, 10th and 17th, respectively.) This outcome reinforces the importance of learning to generalize to unforeseen examples when addressing the challenges of deepfake detection. The competition was hosted by Kaggle and winners were selected using the log-loss score against the private test set. Details on the competition and the final leaderboard are available , and a deep dive on the data and winning models can be found in this new paper about the challenge .

This list shows the top 10 results on the final leaderboard, where participants were evaluated using the black box dataset.

The similarities and differences between the top-performing models also provide insights for future research in this area. The most successful models, including the five winning submissions, all found ways to innovate in the task of deepfake classification. Some of the common themes among the winners include:

Clever augmentations. Many methods used a form of data augmentation that dropped portions of faces - either randomly, using landmarks, or using attention-based networks. Others used more complex "mixup" augmentations , such as blending a real face and an AI-generated one, and then using the blending coefficient as a target label. Another competitor used the WS-DAN model , which uses weakly supervised learning for augmentation. This model takes an image and then either emphasizes or drops discriminative face parts (eyes, mouth, forehead, etc.) to help with the problem of overfitting. Deepfake detection is a more constrained problem than general object detection, but these types of fine-grained visual classification seem to provide an edge when figuring out exactly which parts of a face to drop.

Architectures. All winners used pretrained EfficientNet networks, which were fine-tuned only on the DFDC training data. Most chose the B7 variant of EfficientNet. What differed among competitors was how they used these models: how many they used, and how they combined predictions from an ensemble. The challenge shows that an ensemble approach, which has demonstrated success for many other AI applications, is useful for dealing with deepfakes as well.

Absence of forensics methods. It was interesting to see that none of the top-performing solutions used digital forensics techniques, like sensor noise fingerprints or other characteristics derived from the image creation process. This suggests that either non-learned techniques that operate at a pixel level (or on compressed images) aren't useful for this task or they aren't currently in widespread use among those who entered the DFDC.

Identifying these common characteristics will help researchers improve their models, but the DFDC results also show that this is still very much an unsolved problem. None of the 2,114 participants, which included leading experts from around the globe, achieved 70 percent accuracy on unseen deepfakes in the black box dataset. Facebook researchers participated in the challenge (though they were not eligible for prizes because of our role in organizing the competition). The team's final submission did not appear on the final leaderboard due to run time issues when it was evaluated on that private test set.

As the research community looks to build upon the results of the challenge, we should all think more broadly and consider solutions that go beyond analyzing images and video. Considering context, provenance, and other signals may be the way to improve deepfake detection models.

Once the challenge was completed, we worked with several of our academic partners to stress-test the winning models. We wanted to better understand any specific vulnerabilities in the models before they were open-sourced. The University of Oxford, the National University of Singapore, and the Technical University of Munich all participated and utilized different techniques to try to trick the models. The participants presented methods to trigger false positives and false negatives, and relevant insights on the generalization capabilities of the winning models. These results will be presented at the Media Forensics Workshop @ CVPR.

Building better detection tools to benefit everyone

The issue of deepfakes is an important and difficult one. Like many other types of harmful content, it is adversarial in nature and will continue to evolve and no single organization can solve these challenges on its own. But the DFDC has enabled us to work together, accelerate progress, and ultimately help prevent people from being deceived by the images and videos they see online.

We're grateful for the contributions from all the participants in the challenge, the community, and our partners. As we continue this work, we're committed to continuing this open, collaborative approach to improving deepfake detection tools. Building technology to detect deepfake videos effectively is important for all of us, and we will continue to work openly with other experts to address this challenge together.
In the first video above, clips 1, 4, and 6 are original, unmodified videos. Clips 2, 3, and 5 are deepfakes created for the Deepfake Detection Challenge.

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Deepfake Detection Challenge Results: An open initiative to advance AI

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We're sharing results of the Deepfake Detection Challenge, an open, collaborative initiative to spur creation of innovative new technologies to detect deepfakes and manipulated media. The competition drew more than 2,000 participants, who trained and tested their models using a unique new dataset created for the challenge.

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Norton 2020

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A sample of dataset images used in the Deepfake Detection Challenge.

Deepfake technology can be used create convincing but false video content. Deepfake videos are often designed to spread misinformation online.

For instance, you might view a deepfake video that appears to show a world leader saying things which they actually never said. That could lead to "fake news" that stirs emotion or sways public opinion.

Fake videos have featured subjects like Donald Trump, Barack Obama, and Mark Zuckerberg.

There is heightened concern that deepfake videos could have serious implications during the 2020 election season. People rely on the internet for information, and manipulated videos could potentially influence what they think and how they vote.

That's why it's a good idea to know how to spot deepfake videos. It's not always easy, but here's help.

In this article you'll learn how deepfakes work and how the technology is being used for spreading misinformation and other malicious purposes. You'll also find out how to spot 15 telltale characteristics of deepfakes.

The payoff? It could help protect you against being duped by this evolving technology.

What is a deepfake?

The word "deepfake" combines the concept of deep learning with something that is fake. Deepfakes are a form of artificial intelligence - a compilation of doctored images and sounds put together with machine-learning algorithms.

Deepfake technology manipulates media by creating people that don't exist, or by making it appear that real people are saying and doing things they didn't say or do.

The term first became popular in 2017 after a Reddit user called himself "deepfakes" and shared doctored, pornographic videos. How? He face-swapped celebrity faces onto other people's bodies by manipulating Google's open-source, deep-learning technology.

Audio deepfakes are another form of deception. Here's how it works. Deepfake machine-learning and synthesizing technology creates what are known as "voice skins" or "clones" that enable someone to pose as a prominent figure. An audio deepfake scam is designed to make you believe the voice on the other line is someone you know, like your boss or a client, so you'll take an action - like sending money.

How do deepfakes work?

To spot deepfakes, it helps to know how they work.

One method is through GAN - short for Generative Adversarial Network - which engages in face generation. GAN uses a set of algorithms to train itself to recognize patterns. This training helps it learn real characteristics to be able to produce fake images.

There also are artificial intelligence (AI) algorithms known as encoders that are used in face-swapping and face-replacement technology. This involves running thousands of face shots of two people through an encoder to find their similarities. A decoder, or second AI algorithm, then retrieves and swaps these face images to enable someone's real face to be superimposed onto another person's body.

What is the purpose of a deepfake?

The purpose of deepfake is to use fake content to influence viewers and listeners into believing something happened that didn't.

Deepfakes are often used to spread misinformation and for other malicious purposes. Here's a partial list:

Phishing scams

Data breaches

Hoaxes

Celebrity pornography

Reputation smearing

Election manipulation

Social engineering

Automated disinformation attacks

Identity theft

Financial fraud

Blackmail

In that last case, blackmailers claim they'll release a fake but damaging video of you if you don't give them money or something else of value.

Can you spot deepfake videos?

If you're watching a deepfake video or listening to deepfake audio, will you know if the media is real?

As detection technology advances, so does the quality of deepfake technology. But there are ways to spot deepfakes on your own and with some AI help.

15 ways to spot deepfake videos

AI technology can be difficult to recognize with your eyes alone, so emerging technology can help zero in on characteristics that are harder to see.

Researchers are looking at soft biometrics, such as how a person speaks, along with other qualities in videos to help foster deepfake detection. This focus on soft biometrics is important, because you can watch for these telltale characteristics on your own as well.

Here are 15 things to look for when determining if a video is real or fake.

1. Unnatural eye movement. Eye movements that do not look natural - or a lack of eye movement, such as an absence of blinking - are red flags. It's challenging to replicate the act of blinking in a way that looks natural. It's also challenging to replicate a real person's eye moments. That's becomes someone's eyes usually follow the person they're talking to.

2. Unnatural facial expressions. When something doesn't look right about a face, it could signal facial morphing. This occurs when a simple stich of one image has been done over another.

3. Awkward facial-feature positioning. If someone's face is pointing one way and their nose is pointing another, you should be skeptical about the video's authenticity.

4. A lack of emotion. You also can spot facial morphing or image stiches if someone's face doesn't seem to exhibit the emotion that should go along with what they're supposedly saying.

5. Awkward-looking body or posture. Another sign is if a person's body shape doesn't look natural or there is awkward or inconsistent positioning of head and body. This may be one of the easier inconsistencies to spot, because deepfake technology usually focuses on facial features rather than the whole body.

6. Unnatural body movement. If someone looks distorted or off when they turn to the side or move their head, or their movements are jerky and disjointed from one frame to the next, you should suspect the video is fake.

7. Unnatural coloring. Abnormal skin tone, discoloration, weird lighting, and misplaced shadows are all signs that what you're seeing is likely fake.

8. Hair that doesn't look real. You won't see frizzy or flyaway hair, because fake images won't be able to generate these individual characteristics.

9. Teeth that don't look real. Algorithms may not be able to generate individual teeth, so an absence of outlines of individual teeth could be a clue.

10. Blurring or misalignment. If the edges of images are blurry or visuals are misaligned - for example, where someone's face and neck meet their body - you'll know something is amiss.

11. Inconsistent noise or audio. Deepfake creators usually spend more time on the video images rather than the audio. The result can be poor lip-syncing, robotic-sounding voices, strange word pronunciation, digital background noise, or even the absence of audio.

12. Images that look unnatural when slowed down. If you watch a video on a screen that's larger than your smartphone or have video-editing software that can slow down a video's playback, you can zoom in and examine images more closely. Zooming in on lips, for example, will help you see if they're really talking or if it's bad lip-syncing.

13. Hashtag discrepancies. There's a cryptographic algorithm that helps video creators show that their videos are authentic. The algorithm is used to insert hashtags at certain places throughout a video. If the hashtags change, then you should suspect the video has been manipulated.

14. Digital fingerprints. Blockchain technology can also create a digital fingerprint for videos. While not foolproof, this blockchain-based verification can help establish a video's authenticity. Here's how it works. When a video is created, the content is registered to a ledger that can't be changed. This technology can help prove the authenticity of a video.

15. Reverse image searches. A search for an original image, or a reverse image search with the help of a computer, can unearth similar videos online to help determine if an image, audio, or video has been altered in any way. While reverse video search technology is not publicly available yet, investing in a tool like this could be helpful.

Using technology to spot deepfakes

Several groups are coming up with ways to foster greater AI transparency and protect people from deepfakes. Here are some of them.

Twitter and Facebook. Social media platforms like Twitter and Facebook have banned the use of malicious deepfakes.

Google. Google is working on text-to-speech conversion tools to verify speakers.

Adobe. Adobe has a system that enables you to attach a sort of signature to your content that specifies the details of its creation. Adobe also is developing a tool to determine if a facial image has been manipulated.

Researchers at the University of Southern California and University of California, Berkeley. A notable push is being led by university researchers to discover new detection technologies. Using machine-learning technology that examines soft biometrics like facial quirks and how a person speaks, they've been able to detect deepfakes with 92 to 96 percent accuracy.

Deepfake Detection Challenge. Organizations like the DFDC are incentivizing solutions for deepfake detection by fostering innovation through collaboration. The DFDC is sharing a dataset of 124,000 videos that feature eight algorithms for facial modification.

Deeptrace. This Amsterdam-based startup firm is developing automated deepfake detection tools to perform background scans of audiovisual media - similar to a deepfake antivirus.

U.S. Defense Advanced Research Projects Agency. DARPA is funding research to develop automated screening of deepfake technology through a program called MediFor, or Media Forensics.

Deepfakes moves, countermoves, and you

Here's the problem. Creators of detection algorithms respond to the latest deepfakes with their own new technology. In turn, the people behind those deepfakes respond to the new detection technology.

While the battle goes back and forth, it's a good idea to know how to spot deepfakes - at least some of the time - and take steps not to be fooled. It helps if you're skeptical about video you see on the internet and careful about sending funds until you're sure the voice on the other end of the line is legit.

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How to spot deepfake videos - 15 signs to watch for

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A sample of dataset images used in the Deepfake Detection Challenge. Deepfake technology can be used create convincing but false video content. Deepfake videos are often designed to spread misinformation online. For instance, you might view a deepfake video that appears to show a world leader saying things which they actually never said.

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Websites for checking things:

Media misinformation casebook: https://mediamanipulation.org/

Google Fact-check explorer: https://toolbox.google.com/factcheck/explorer

Tineye: reverse image search: https://tineye.com/

Reverse video search: Tineye, Google image search,

Types of media bias: https://www.allsides.com/media-bias/how-to-spot-types-of-media-bias

Content authenticity initiative: https://contentauthenticity.org/ (Adobe)

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Mitigating misinformation - whose job is it?

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Mitigating misinformation - whose job is it?

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Government - regulations

Social media platforms - reduce exposure and have rules for content - but do these actually work? They direct people to information, remove some content (but not a lot)

People themselves - learn to filter

It's also unavoidable - we just have to live with it. So education is key.

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1. Do a quick search on the source - use a fact-checking website,

2. Look for standards/ethical guidelines,

3. Transparency about reporting, funding, ownership, advertising, authors

4. System for handling errors/mistakes/feedback

5. Assess news coverage and look for other news articles and their quality

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Misinformation = umbrella term for:

1. Poor journalism (clickbait, misleading headlines, incorrect facts, dumbed-down stories)

2. Look like news but are ads

3. Spun stories

4. Fabricated stories

5. Made up satire stories

6. Fake news to discredit news they don't like

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1. Misinformation is prevalent/everywhere - over 50% knowingly encountered misinformation in one week in Australia in 2018-2021.

2. Social media permeates. This was worst on social media such as Facebook and Twitter, and people experienced it more the more social media platforms they used. 78% of people get news through social media.

2. Australians lack confidence in dealing with misinformation - they can't identify it, or know if they see it. Governments, news media companies and social media companies should step in. Most people don't do anything about misinformation, although younger people may challenge it (low numbers).

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John Green on Lateral reading - this is the whole playlist: https://www.youtube.com/watch?v=L4aNmdL3Hr0&list=PL8dPuuaLjXtN07XYqqWSKpPrtNDiCHTzU
Video #1 at 11:48 talks about evaluating laterally - not just superficially
Video #2 specifically talks about rigorous fact-checking: 1. who is behind this information? 2. what evidence backs up this claim? 3. what do other sources say about this?
Video #3 two sites to compare are:

https://acpeds.org/

https://www.aap.org/

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Check Yourself with Lateral Reading: Crash Course Navigating Digital Information #3

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"We define media manipulation as a process where actors leverage specific conditions or features within an information ecosystem in an attempt to generate public attention and influence public discourse through deceptive, creative, or unfair means."
Case studies - might be useful as examples in a class: https://mediamanipulation.org/case-studies

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Resources

A Ted Talk on how How fake news does real harm by Stephanie Busari (6 minutes).

Click-Sick Series:

Part 1: Why sharing isn't always caring. On the trail of COVID-19 Misinformation. Science Friction (Radio National).

Part 2: The hidden political forces pushing pandemic conspiracies. Science Friction (Radio National).

Part 3: Can 'wellness' make you...sick? Science Friction (Radio National).

MediaWise: Poynter’s digital media literacy program
6 tips to help spot misinformation online - a great summary of all the tools that we learned in class, also is up-to-date, June 2022

References and further reading

Mainstreaming of conspiracy theories and misinformation
- reveals how tight community bonds can perpetually generate new misinformation regardless of factual information.

Wardle, C. & Derakhshan, H. (2017). Information Disorder: Toward an interdisciplinary framework for research and policymaking, Council of Europe.

Notley, T., Chambers, S., Park, S. & Dezuanni, M. (2021). Adult Media Literacy in Australia: Attitudes, Experiences and Needs. https://westernsydney.edu.au/__data/assets/pdf_file/0007/1824640/Australian_adult_media_literacy_report_2021.pdf

Park, S., Lee, J., Atkins, S. & Su, J. (2021). Media Literacy in Australia: A Qualitative Study. https://www.canberra.edu.au/research/faculty-research-centres/nmrc/research/Media-Literacy-in-Australia-A-Qualitative-Study

Park, S., McCallum, K., Holland, K., McGuinness, K., Lee, J., Fisher, C. & John, E. (2022). COVID-19: Australian News & Misinformation Longitudinal Study. Canberra: News Media Research Centre, University of Canberra.

Park, S., Fisher, C., Lee, J. & McGuinness, K. (2020). COVID-19: Australian news and misinformation. Canberra: News & Media Research Centre. https://www.canberra.edu.au/research/faculty-research-centres/nmrc/publications/documents/COVID-19-Australian-news-and-misinformation.pdf

Nielsen, R. (2022). Can news help?  New evidence on the links between news use and misinformation https://reutersinstitute.politics.ox.ac.uk/news/can-news-help-new-evidence-links-between-news-use-and-misinformation

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So how much of your thoughts are really yours and how much are manipulated by marketing?

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Traditional ads
Native ads
Hoax ads
Political ads

Have varying levels of transparency - you know you are looking at an ad, so you can decide credibility

Who created it?

What is the purpose?

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Checkology is a free e-learning platform with engaging, authoritative lessons on subjects like news media bias, misinformation, conspiratorial thinking and more. Learners develop the ability to identify credible information, seek out reliable sources and apply critical thinking skills to separate fact-based content from falsehoods.

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"My experience is what I attend to" - William James
-  1p papers that are cheap so lots of people buy them but will lose money unless advertisers buy space ...much more racey than 6p paper that is serious and relies on people buying it to make money

- web limits on advertising - not many! or lacking

- on the web there is lots of advertising that masquerades as not advertising...think things that draw you in to watch a video or enter a competition, or answer a survey...or find somewhere on a map

- the web is free - or is it? Pay with:

- data, attention, giving a lot of access to ourselves and our thoughts/ideas - this open to influence, leading to differences in how we may otherwise live our lives (e.g. buy more things and so on).

- loss of ability to focus deeply because ad-supported mediums need a receptive audience - leads to "Casino effect" where you're super receptive and constantly clicking and following links

A lot of our attention these day is not in the "now" as we spend a lot of time onscreen in a sim reality - this means it's important we understand the motives of the people creating the stuff we are consuming

A realized life requires decisions that are ours

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Advertising Hazards: Your Attention is a Commodity That Can Be Manipulated | Tim Wu | Big Think

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3 types of branded content:

1. Sponsored - the newspaper (or publisher) writes the content to match the style, not the advertiser

2. Custom - created in collab with publisher and brand to feature topics of joint control

3. Syndicated - brand produced, hosted by the publisher

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Discover the story of David Cowan, the deaf ASL interpreter, providing a bridge to the hearing world.

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How do you know to trust an ad?

1. who is serving the ad? Icon at top

2. quality of ad - crap clickbait is dodgy

3. where it appears? (what website)

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Hendricks V.F., Vestergaard M. (2019) The Attention Economy. In: Reality Lost. Springer, Cham. https://doi.org/10.1007/978-3-030-00813-0_1

Goldhaber, M (1997). Attention Shoppers! The currency of the New Economy won't be money, but attention -- A radical theory of value. Wired Magazine. Retrieved 19 Sept 2021: https://www.wired.com/1997/12/es-attention

Sirrah, A (2019): Guide to Native Advertising. Tow Centre for Journalism, Columbia Journalism Review. 6 Sept.  https://www.cjr.org/tow_center_reports/native-ads.php

Barbara Fister from Project Information Literacy talk about her study of Information Literacy in the Age of Algorithms. https://projectinfolit.org/pubs/algorithm-study/pil_algorithm-study_2020-01-15.pdf

Video

The Gender Shades Project shows how AI technology that is the foundation of algorithms can be biased.

Further Reading

Wu, T (2017). The Attention Merchants: The Epic Struggle to Get Inside Our Heads (2017). Atlantic Books.

The Social Dilemma: https://www.thesocialdilemma.com/the-film/ (Available on Netflix).

The Gruen Transfer: ABCiView. https://iview.abc.net.au/show/gruen

Question Everything: ABC iView. https://iview.abc.net.au/show/question-everything

References

Andrew, D (2017): Programmatic trading: the future of audience economics, Communication Research and Practice, 5:1, 73-87, DOI: 10.1080/22041451.2019.1561398

Breiner, J (2021): Risks and Rewards of Native Advertising. International Journalist’s Network (IJNet). https://ijnet.org/en/story/risks-and-rewards-native-advertising

Digital 2022 Australia: Online Like Never Before. https://wearesocial.com/au/blog/2022/02/digital-2022-australia-online-like-never-before/. We Are Social, viewed 28 March 2022.

Duhigg, C (2012). How Companies Learn your secrets. The New York Times Magazine, February. https://www.nytimes.com/2012/02/19/magazine/shopping-habits.html?pagewanted=all&_r=0%20-%20video

Heller, B and Bar-Zeev, A (2021): The problem with immersive advertising: in AV/VR, Nobody Knows You Are An Ad. Journal of Online Trust and Safety, October 2021. Doi:10.5401/jotsv1i1.21 (Provided as a pdf – open access). https://tsjournal.org/index.php/jots/article/view/21/10
(Links to an external site.)

IAB Australia (2017): The Native Playbook. https://iabaustralia.com.au/wp-content/uploads/2020/08/IAB_Native-Advertising-Playbook_Oct-2017.pdf

Smith, R; Bounegru, L and Gray, J. (2021). How anti-vaccination websites build audiences and monetize misinformation. First Draft 10 March https://firstdraftnews.org/articles/antivaccination-audiences-monetize/  Retrieved 6 December 2021

Rogers, C (2017): What is programmatic advertising? A beginner’s guide. Marketing Week, 27 March. https://www.marketingweek.com/programmatic-advertising/

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Cranky Uncle: https://crankyuncle.com/game/

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Play GO VIRAL! | Stop Covid-19 misinformation spreading

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New online infodemic game shows how to spot coronavirus misinformation in 5 minutes. Based on Cambridge University pre-bunking research. Play Go Viral!

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Save the Troy Library "Adventures In Reverse Psychology"

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Padlet about wellness influencer tactics: https://padlet.com/ucedinnovation/xmbrqnfw71r0yn8e

How influencers are similar to conspiracy theorists

1. heros journey - underdog to having found the solution

2. constructed friendships - share personal info to gain trust

3. Celebrities talk about them/blow off steam with conspiracies

Influencer that I think of - Medical Medium

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What do social media influencers and conspiracy theorists have in common?

It's a question that sociologist Stephanie Alice Baker from City, University of London had not seriously considered before the COVID-19 pandemic.

But while investigating how social media influencers were responding to the pandemic she noticed that the techniques lifestyle gurus used to promote themselves were also being adopted by conspiracy theorists.

These were techniques that Dr Baker had described in a recently published book titled Lifestyle Gurus: Constructing Authority and Influence Online.

"I think these are just savvy marketing techniques. [Influencers] are not all trying to dupe the public, but they are brands," Dr Baker says.

"It's important to see conspiracy theorists through the same lens."

Both influencers and conspiracy theorists could only thrive in a "low-trust society", she says.

"There's no way these techniques would be nearly as effective if you didn't have a climate of distrust [in authority figures] and if people were not generally uncertain and fearful.

"That combination allows influencers to use the techniques I've outlined to establish authority."

A hero's journey

Both influencers and conspiracy theorists present themselves as anti-establishment underdogs on a "hero's journey", comparable to Biblical tales of good versus evil, Dr Baker says.

"They don't totally manufacture these narratives, but they cherry-pick information and it's woven with anecdotal evidence."

"It's great storytelling, really."

Dr Baker cites the case of the viral conspiracy theory film Plandemic, which promoted misinformation about COVID-19 and was eventually removed from Facebook and other social networks for violating their guidelines.

The star of the film, Judy Mikovits, was presented in the film as a "whistle-blower and scientist who'd been wronged by [US government scientist Dr Anthony] Fauci and the scientific community in general," she says.

The timing of the film also just so happened to coincide with the publication of Mikovits' book.

"The viral spread [of Plandemic] was not just about sharing misinformation ... it is a good example of promoting a political influencer and establishing her identity as an underdog."

Former Instagram star Belle Gibson, who made hundreds of thousands of dollars from false claims her diet cured her brain cancer, similarly gained a huge following online by positioning herself as a rebel who defeated the odds against medical advice.

Colin Klein, a philosopher at the Australian National University, has also observed strategies that conspiracy theorists use to convince others they are telling the truth.

Professor Klein, who studies the way people debate and promote conspiracy theories on the popular online forum Reddit, says that many users described their newfound belief in a conspiracy as a hero's journey or "religious awakening".

But he found that such users had not actually been randomly enticed by misinformation, but were rather drawn to the conspiracy forum by their pre-existing views.

"There's a tempting picture ... that people are largely passive, that there's misinformation around and people accidentally get exposed to it," he says.

"In fact many of these things only work because they're falling on people who are predisposed [to conspiratorial thinking]. They either have these narratives of the world or they're susceptible to it."

Constructed friendships

Dr Baker says prominent conspiracy theorists established trust by positioning themselves as equals or friends directly communicating with their audiences.

They share personal information about their daily routines, sometimes multiple times a day, in a performance of manufactured authenticity.

This helps them quickly build trust and relationships with people they have never physically met.

"Whereas mainstream authority figures are distrusted for various reasons, these online users are trusted ... for being just like us," Dr Baker says.

"It fosters this degree of trust because people feel they know them like they know a friend.

"We know from advertising that people are much more likely to trust their friend's advice than some professional or paid celebrity endorsement."

Click-Sick: The impact of health misinformation

If you share dodgy medical advice online, how much damage can it do? On Science Friction:

Edward Hurcombe, a researcher at the Queensland University of Technology's Digital Media Research Centre, has studied how 5G- and COVID-19-related conspiracy theories migrate from fringe groups on Facebook to mainstream news outlets.

The study, published in the Media International Australia journal in August, found the major turning point came when celebrities and sports stars amplified the misinformation to their millions of followers on social media, even when they didn't directly endorse it.

"We found that it really became a story in the mainstream consciousness and really started getting attention on Facebook after celebrities like [American singer] Keri Hilson and Woody Harrelson posted about 5G and COVID-19 ... raising this possibility of 'are they connected?'" Dr Hurcombe says.

"News articles started reporting on the conspiracy because celebrities were talking about it. Celebrities talking about a conspiracy theory [is] almost inherently newsworthy."

Although many of the news articles were critical of the celebrities involved, conspiracy theorists used this coverage to indicate that they were "close to uncovering some deeper truth," he says.

Blowing off steam with conspiracies

The ANU's Professor Klein says it isn't clear how many people who discuss conspiracies on Reddit genuinely believe them.

He argues that conspiracy theories serve unique purposes for individuals, some of whom just use them to "blow off steam".

"People are coming up with hypotheses, they're debating them. That feeling of being a part of a community and having that collaborative project is actually really important," he says.

People participate less in social clubs like the RSL and party politics than they did 30 years ago, Professor Klein says, which means that offline life had become more fragmented.

That makes the mere act of discussing conspiracies "a valuable thing in its own right", he says, even if they didn't believe in them, as it allows some form of interaction.

He likened people chatting about conspiracies online to a group of friends discussing the AFL grand final at a pub.

"Imagine someone saying at the pub 'Geelong's going to go all the way next year'. Do they really believe that in the way I believe in the theory of gravity?" he says.

"It's responsive to what's going on, but it's also an expression of being a [Geelong] Cats fan.

"It's some mix of belief and hope and expression of who you are and what your worldview is. It's hard to disentangle these."

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What do lifestyle gurus and conspiracy theorists have in common?

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Both present themselves as anti-establishment underdogs, comparable to Biblical tales of good versus evil. But it's not just this 'hero's journey' narrative that helps spread conspiracy theories and extend influencer reach online.

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The Technology and Social Change Project was active from 2019-2023. The Media Manipulation Casebook was a major publication of the TaSC project, and will remain fully accessible at this URL going forward, but will no longer be actively updated.

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Plandemic, a 26-minute trailer video about coronavirus conspiracy theories, went viral in May 2020 because of distributed amplification. In response to its high viewership, major social media platforms moderated Plandemic and prepared for the full-length video. The platforms' efforts slowed the spread of Indoctornation, the anticipated 75-minute movie.

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Just in case I become an influencer :)

Notes from how wellness culture and lifestyle influencers spread misleading advice. Esp COVID 19 :
- Combine far right with wellness (this was originally progressive)

Tactics:

- informal modes of address

- personal updates/anecdotes

- authentic alternative to mainstream

- open to supposed private lives

- equals with followers

- supportive friend

- attract people disillusioned with institutions

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Avoid amplification

Audiences receive information from a range of sources, including the influencers they follow. But influencers can be targeted by those trying to spread disinformation in order to their false stories. Influencers in Australia and New Zealand are not immune, so we've developed this guide to help you protect your voice.

Dodge disinformation

Social media celebrities can unwittingly be used as a powerful in promoting false information and conspiracy theories. Mis- and disinformation relating to the Covid-19 pandemic, bushfires, 5G and other conspiracy theories has proliferated across the globe.

Spread the knowledge

Anyone with a platform has a voice. And anyone with a voice has the power to influence. The lessons in this toolkit and the conversations from First Draft's seven-day social media campaign will help all of us play a part in cleaning up our information environment.

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Protect your voice: a toolkit for influencers and celebrities in Australia and New Zealand - First Draft

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Our new toolkit aims to help influencers understand and avoid spreading misinformation.

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Also from the News Literacy Project: The Sift
My role? Padlet on librarian role

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Resources of interest (Open Source)

First Draft: Too much information: a public guide to navigating the infodemic.

https://firstdraftnews.org/long-form-article/too-much-information/

First Draft Protect Your Voice Toolkit:

https://firstdraftnews.org/tackling/protect-your-voice-a-toolkit-for-australian-influencers-and-celebrities/

The Media Manipulation Casebook: Technology and Social Change Project (TaSC), Harvard Kennedy School

https://mediamanipulation.org/research/tracking-social-media-takedowns-and-content-moderation-during-2022-russian-invasion

Donovan, J (2019):How memes got weaponized: a short history. MIT Technology Review, October 24. https://www.technologyreview.com/2019/10/24/132228/political-war-memes-disinformation/

Crabb (2021): Facebook or YouTube? What does your favourite social media site say about you? ABC Online, 21 June: https://www.abc.net.au/news/2021-06-28/annabel-crabb-australia-talks-social-media/100241888

First Draft: How Communities can help ensure quality information is share widely during the pandemic. April 23, 2020.
https://youtu.be/f6mnt3L5gbU

This video is part of the publicly-available webinars available on the First Draft website: https://firstdraftnews.org/webinars/

Digital Citizenship and Libraries

National and State Libraries Australia (NLSA): Digital Citizenship Resources

https://www.nsla.org.au/resources/topic/digital-citizenship?page=0

References

Al Jazeera (2021): Facebook failing to check hate speech, fake news in India: Report. 21 October. https://www.aljazeera.com/news/2021/10/2

Roozenbeek J, Schneider CR, Dryhurst S, Kerr J, Freeman ALJ, Recchia G, van der Bles AM, van der Linden S. 2020 Susceptibility to misinformation about COVID-19 around the world. R. Soc. Open Sci. 7: 201199. http://dx.doi.org/10.1098/rsos.201199

Wawrzuta D, Jaworski M, Gotlib J, Panczyk M.  Characteristics of Antivaccine Messages on Social Media: Systematic Review J Med Internet Res 2021;23(6):e24564. doi: 10.2196/24564

Aubrey, S (2021): ‘So many are unethical’: Influencers forced to face up to the rules of advertising. Sydney Morning Herald online, 1 May. https://www.smh.com.au/lifestyle/life-and-relationships/so-many-are-unethical-influencers-forced-to-face-up-to-the-rules-of-advertising-20210428-p57n6d.html

Influencer Marketing: Social Media Influencer Market Stats and Research for 2021. Insider Intelligence, 7 Jan.  https://www.businessinsider.com/influencer-marketing-report?r=AU&IR=T

Spangler, T (2020): Miquela, the Uncanny CGI Virtual Influencer, Signs With CAA (EXCLUSIVE). Variety.com. https://variety.com/2020/digital/news/miquela-virtual-influencer-signs-caa-1234599368/

Klein, M (2020). The Problematic Fakery Of Lil Miquela Explained—An Exploration Of Virtual Influencers and Realness. Forbes.com. https://www.forbes.com/sites/mattklein/2020/11/17/the-problematic-fakery-of-lil-miquela-explained-an-exploration-of-virtual-influencers-and-realness/?sh=630c6601b8df

Crabb (2021): Facebook or YouTube? What does your favourite social media site say about you? ABC Online, 21 June: https://www.abc.net.au/news/2021-06-28/annabel-crabb-australia-talks-social-media/100241888

Sharma, B., Lee, S. S., Johnson, B. K., Green, C. S., Bowman, N. D., Greitemeyer, T., … Greitemeyer, T. (2022). The Dark at the End of the Tunnel: Doomscrolling on Social Media Newsfeeds. Technology, Mind, and Behavior. https://doi.org/10.1037/tmb0000059. For a 1 minute overview of the research, watch here: https://tmb.apaopen.org/pub/nn9uaqsz/release/2#nhu3yef3uys

Culliford & Heath, 2021: Facebook knew about, failed to police, abusive content globally – documents. Reuters, 26 October.

Kelsey-Sugg, A & Docherty, S (2021): 'The wild, wild west of advertising': the business of distinguishing personal from paid posts. ABC News 25 May, accessed 28 March 2022. https://www.abc.net.au/news/2021-05-05/influencer-marketing-breaching-standards-guidelines-to-help/100106396

Mannheim, M (2021): Australia Talks data shows we don't trust Instagram influencers, but advertisers rely on them increasingly. ABC News 28 May, accessed 28 March 2022. https://www.abc.net.au/news/2021-05-27/australians-say-they-do-not-trust-influencers-but-do-they-really/100164654

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Online content that is seriously threatening , harassing, intimating or humiliating

More subtle that schoolyard bullying

How to respond:

- don't reply to the bully

- block the person

- keep all emails and messages they send

- report the bullying to someone else

- report to online platform and then the e-safety commissioner (Australia)

- recovering from cyberbullying can take a long time

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- Keep personal information to a minimum – use privacy functions and control who sees the content

- Be mindful of strangers who befriend you

- Create complex passwords that include symbols, use password managers, use multi-factor authentication

- Minimise the use of unsecure networks

- Do not click on any links in an email or SMS if you are not sure of the source

- Look for the ‘padlock’ and https in the URL field and use established, well known sites

- Do not respond to threats or harassment but keep a record (screen shot, download etc) and tell someone

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Main scams: scams, fraud, identity theft, cyberbullying/ stalking, exposure to illicit materials...

98% Aussies have been exposed to a scam or unsolicited communication - esp. older people

Most people would like more control - many people can't unsubscribe or are contacted after subscribing.

Phone Calls - very high rate.

Scam watch - Phishing highest - how to recognise and report scams

Media literacy related to media use in staying safe, recognising a scam, and reporting

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Also Netsafe (NZ) : https://www.netsafe.org.nz/
Reporting scams in NZ: https://www.consumerprotection.govt.nz/general-help/consumer-laws/online-safety-laws-and-rules/

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New Zealand is committed to minimising online harms. The Digital Safety Group at Department of Internal Affairs responds to violent extremist content and child sexual abuse material online.

Internal Affairs' Digital Child Exploitation Inspectors work with local and overseas agencies swapping intelligence about people using the Internet to share objectionable material. Our Inspectors have the expertise to track down and catch offenders.

Digital Violent Extremism Investigators respond to content that promotes or encourages violent extremism and where necessary work with online content hosts to have such material removed.

If you are concerned that you have seen something that is objectionable under the Film, Videos and Publication Classification Act 1993, please report it using our online form:

Report harmful content *

* If you have come across or been sent either the livestream video or manifesto related to the recent mass shooting in Buffalo please report it here – it is illegal to share content which has been classified as “objectionable” (16 May 2022).

Countering violent extremism online
Consultation: Standardising classification for commercial video on-demand content
Support and helpline services if you or someone you know has viewed the 15 March 2019 Christchurch video

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Prevention of online harms - dia.govt.nz

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New Zealand is committed to minimising online harms. The Digital Safety Group at Department of Internal Affairs responds to violent extremist content and child sexual abuse material online.

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When businesses start implementing stricter password policies, they often start with password managers so employees can store their passwords in an encrypted, relatively secure environment. One reason is that employees must add password management to their to-do list. Moreover, password vaults still require users to log into each app, which can add up to a lot of wasted time. For these reasons, most organizations quickly outgrow password managers.

Single Sign-on (SSO) is a secure solution that allows users to log into multiple accounts -both on-prem and cloud - just once using one set of credentials. It thus provides more seamless and secure access across multiple systems.

SSO is usually part of an Identity and Access Management (IAM) solution that uses the company's directory, such as Microsoft Active Directory, Azure Active Directory, or a directory provided by the SSO solution. It also uses standard, widely accepted protocols, such as SAML or OAuth, and technologies like digital certificates to provide enterprise-level security.

SSO is more secure than password vaults, since it reduces the frequency of logins and the number of credentials stored. Further, passwords are not passed around. Instead, after login, SSO passes tokens to the app or website requesting authentication. It thus reduces the attack surface and minimizes the possibility of cyberattacks. SSO is also easier to use than password vaults, and eliminates the need to maintain multiple passwords, thus easing the burden on users.

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When businesses start implementing stricter password policies, they often start with password managers so employees can store their passwords in an encrypted, relatively secure environment. One reason is that employees must add password management to their to-do list. Moreover, password vaults still require users to log into each app, which can add up to a lot of wasted time.

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Baccarella, C. V., Wagner, T. F., Kietzmann, J. H., & McCarthy, I. P. (2018). Social media? It's serious! Understanding the dark side of social media. European Management Journal, 36(4), 431-438. doi:https://doi.org/10.1016/j.emj.2018.07.002

Evans, D. & Schmalensee, R. (2016). Matchmakers: The New Economics of Multisided Platforms. Harvard Business Review Press.

Head, A., Fister, B. & MacMillan, M. (2020). Information literacy in the age of algorithms, Project Information Literacy Research https://projectinfolit.org/pubs/algorithm-study/pil_algorithm-study_2020-01-15_algo-figure.png

Kietzmann, J. H., Hermkens, K., McCarthy, I. P., & Silvestre, B. S. (2011). Social media? Get serious! Understanding the functional building blocks of social media. Business Horizons, 54(3), 241-251. doi:https://doi.org/10.1016/j.bushor.2011.01.005

Parenting, Media, and Everything In Between: 11 Social Media Red Flags Parents Should Know About https://www.commonsensemedia.org/blog/11-social-media-red-flags-parents-should-know-about

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Just as robots replaced the factory workers, and the drive-through workers were outsourced to India, those employed to yacht the world, drink fat-burning smoothies and just be attractive are getting furloughed too. Stars-they're just like us.

Influencers are being upended by Virtual Influencers, and these CGI avatars may be disrupting something even more integral: truth.

We've already been mystified by their soft edges and been made uncomfortable with their attractiveness. There's Shudu, the world's first digital supermodel, Blawko, the digital tattooed "fuccboi," and Miquela Sousa AKA Lil Miquela, the Princess Leia-bunned poster child of the movement. They now come in an array of options. Human or alien. Political or apolitical. White or BIPOC. Lean or plus-sized. Choose your player.

Insecure by your own success? These names have been sponsored by the likes of Chanel, made out with Bella Hadid, and graced the covers of Esquire. This year, Lil Miquela signed with CAA and is projected to earn over $10M. Someone who doesn't exist is more successful than you. Let that sink in.

Virtual Influencers' success thus far is clear from the business POV. Brands are attracted to the novel like bugs to a light. Virtual Influencers are controllable and pose no risk-Miquela doesn't have seven-year-old racist tweets to blow her film prospects. Further, it's proclaimed that Virtual Influencers command three times higher engagement than a human Influencer. And most apparently, they're nice to look at. So why not shell out for a sponsored post or invest tens of millions in their animators?

As these fictitious personas begin to thrive in the uncanny valley and spin off business implications left and right, what's just as noteworthy is what they silently scream about today's cultural climate. As the headlines mount, their prices climb, and more brands test drive the concept, we should take a beat.

How duped are we?

Virtual Influencers are not that original of a concept. Damon Albarn and Jamie Hewlett have been successfully animating their virtual band Gorillaz for over 20 years, snagging Grammy noms and even appearing on MTV's . But this was only after Alvin and the Chipmunks cleared the way back in 1958. Making them sexy isn't new either-check out Jessica Rabbit. And brands playing in this space is old hat. GEICO has been pulling the strings of their own virtual influencer, Martin the accented gecko, for over 15 years now.

What Tony the Tiger is to cable, Miquela is to Instagram. The only difference is that today's animated characters now have a life worth following. Miquela also isn't owned by the man... and she supports #BLM. It's only fitting that in 2020 our mascots are progressive free-agents hustling in the gig economy.

Christopher Travers, founder of VirtualHumans.org, the database for all things Virtual Influencers, says they "represent the ongoing merger of humanity and the internet." He believes, "Virtual influencers enhance and humanize the world's relationship with digital experiences." But what we're humanizing-our online convergence-may be unfit for personification. Perhaps it's a step too far.

In a 2018 interview with the publication Business of Fashion, Miquela shared, "I definitely wouldn't say my identity is crowdsourced. I'm an artist and have expressed opinions that are unpopular and as a result have cost me fans." Her literally crowdsourced answer is the work of Brud, the transmedia studio which is valued upwards of $125M. And it's this untruth-"I'm not crowdsourced"-a thread of deceptiveness, that when pulled, doesn't seem to end. When media literacy costs elections, we need more truth, not deception.

Miquela claims she's an AI robot. A fictitious entity, acting as a human, manifested as a life-like robot. "I'm a dude playing a dude disguised as another dude." Of her millions of young followers, how many of them are fully conscious of her misrepresentation? Truth in Advertising, the industry watchdog, is calling for FTC reform. Scroll by fast and you can't spot the difference. And that's the very draw of Miquela, over the cartoonish Seraphine. Miquela attempts to pass. It's this effort that's critical. Any less realistic, it's try-hard. Any more realistic, it's nefarious. But while the majority notice the fakery, they're still left confused.

"There needs to be internet literacies for young people if Virtual Influencers are going to become more common," advises Dr. Jamie Cohen, an expert on digital culture. "The internet did not come with an instruction manual and we accept certain unique things to occur in that space, but knowledge of how Virtual Influencers operate, who operates them, why they operate them, and who they are for, is crucially important to a consumer culture."

Beyond transparency, another popular demerit of Virtual Influencers is their bar of unrealistic beauty. As Kaitlyn Tiffany for Vox puts it, "They are physically perfect women made of pixels, standing in for women who have long been pressured to become physically perfect, without the advantage of that even being possible." On a photo of Miquela posing with an actual model, one Lil Miquela fan account comments, "the robot more pretty." Miquela's handlers at Brud rush in to disagree with the commenter, perhaps recognizing "we went too hot."

Miquela isn't just competing with models, but real underrepresented talent. The fact that, "a glorified sticker", as writer Rob Horning puts it, is now competing with actually marginalized people is sinister. CGI-diversity is mistaken with real diversity. One online commenter asks, "Why rep a real woman of color when you can have a fake one that you can totally control everything about them?" To those leaning in, this is insincerity without awareness-a score against Team Human. And we're doing this to ourselves. It's self-deception at its worst.

Another more operational untruth is whether Miquela is even AI. That Brud claims to be a computer software, robotics and AI company, yet according to LinkedIn exclusively employs visual effect artists and content producers is another distortion. It's a collage of misrepresentation.

We don't seem to mind, though. What we're engaging in is kayfabe-Miquela and The Undertaker are one in the same. It's all pretend. We suspend our belief, and listen to Miquela's answers as if she was actually opinionated and freckled. VC's are spellbound too, just by sticker companies.

The world of Virtual Influencers permits adult playtime.

There's an irony here. Social media has become the bungalow to house our curated and filtered highlight reels, and Influencers, built up from trust, have sold out, pushing products and cashing oversized checks. Meanwhile, starlets like-for-like, and pay to inflate their metrics and procure verified checkmarks. This entire ecosystem is drenched in fiction. That GenZ is willing to engage with a flagrantly fake person should not be surprising. The illusion of "attainable beauty" is just the tip of the iceberg. It's all fake. We love it. And we can't get enough.

Miquela is a caricature of many users' own Insta presence. We strive for and fawn over her exaggerated life, but ignore the warning: it's not real.

Travers of VirtualHumans.org points out, "Virtual Influencers do not make social media fake-social media platforms predicated on celebrating fictional ideologies of life grew to create an environment now ripe for fictional characters to step in and succeed." One ushered in the other. But what's fascinating is that GenZ is both leaning into and rejecting this state of affairs. It's a push and pull.

When sweatpants-wearing, high school TikTokers rise to fame in their parent's kitchen and begin to dethrone The Kardashians, Jenners and Ratajkowski's of the world, change is afoot. GenZ is recognizing these people are in fact nothing like them. They never were. And who even wanted that? Those objects on our screen are not as close as they appear. What is the Influencer Economy, but just the redistribution of stardom and power from legacy names? And that a Brazilian, 20-year-old Miquela can be perceived and valued as more relatable than the traditional A-listers is significant.

The fake is more real.

According to a study by social content agency, Fullscreen, nearly a fourth of GenZ and Millennials would describe a Virtual Influencer as "authentic."

Some ask her, "Do you feel pain? Can you actually eat food? Do robots dream of electric sheep?" Others ask what her favorite song is or where she currently lives. She doesn't reply. Whether one is playing along or feels shrewd enough to try to out her, she's under our skin. For Dr. Cohen, the perennial question is, "Who is the one playing us, or are we the one playing the game?"

The yearning for this to all be real reveals a desperation. Miquela, unaffected by politics, debt, COVID-19 and bullies is what's truly aspirational. She's our best selves, stripped of our messy human shortcomings, disappointing flaws, and soft vulnerabilities-the inverse of a zombie. Being called less-or more-attractive than the real model beside her doesn't affect her ego. She has none. Isn't that the dream?

"We should be concerned by a Virtual Influencer's ability to create parasocial relationships," warns Dr. Cohen. When something is designed to take more from us than give back, we encounter trouble. When we close Instagram feeling worse, not better, after exploring Miquela's life, we hurt ourselves. Tell Siri you love her as a joke, it doesn't care. Not a stretch, our attraction toward Miquela hints at what's down the pike with the future of intimacy.

What is Miquela but the sex robot in each of our pockets?

According to Dr. Stacy Thayer, Professor of CyberPsychology at California Lutheran University, "[Virtual Influencers] create a fantasy, but has the potential to move us further away from acceptance of our own realities."

Miquela is a fantasy, but she's not to be mistaken with escapism. A sci-fi movie is escapism. Miquela captures, not frees us. Her looks and role straddles the fence between real and fake-or rather, there is no longer a fence, but just spillover. We don't know what to make of this early mess. As a result, we just stare and yell hypnotized-contrasting our looks to CGI beauty, getting lost in their fictitious feuds, and applauding the diversity and stretch marks of the plus-sized ones.

We're not entirely lost, though. There was proper backlash for when Miquela shared her "sexual assault encounter" in a rideshare via a vlog-one which never occurred in reality. PR at Lyft should be peeved. What's the legal basis for slander here? Also, can this piece be considered libel as it may affect Miquela's career prospects? In any case, slim chance law catches up this fast.

Publicity around Virtual Influencers should not be used as a proxy for their success or justification of their existence.

We're not too far gone. "Virtual influencers are a neutral content medium," says Travers. "Like podcasts, like short-form videos, or like selfies, Virtual Influencers can be used for whatever purpose the creator desires."

As more Virtual Influencers are spawned and Samsung works on their virtual human project, NEON, we should focus on intent. It's not that these characters exist, it's what we decide to do with them. Some are already leveraging the concept to educate people about The Holocaust, memorializing victims to share their stories after they pass. Others are approaching virtual avatars as characters to confide in as they don't judge and are more accessible and affordable than talk therapy.

Our way through is to remember what we've been taught. Let's not judge Virtual Influencers because they're different. Let's look past appearances. Rather, let's consider their character. Not how they look, but how they behave, and why they're here? What is their purpose, and how do they help us?

Technology is a tool to better understand ourselves, after all. A black mirror.

Miquela isn't real. Which begs the question, are we any better?

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The Problematic Fakery Of Lil Miquela Explained-An Exploration Of Virtual Influencers and Realness

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Just as robots replaced the factory workers, and the drive-through workers were outsourced to India, those employed to yacht the world, drink fat-burning smoothies and just be attractive are getting furloughed too. Stars-they're just like us. Influencers are being upended by Virtual Influencers, and these CGI avatars may be disrupting something even more integral: truth.

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