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Need to reteach a media literacy skill, but don't know where to start?

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Learn to identify a politically-motivated claim and fact-check it while learning about the impact of the Inflation Reduction Act on energy production. Students will also learn to narrow searches to find original Congressional documents more quickly. For more information and to complete the lesson, go here. (45 - 60 Mins)

Introduce students to how data and statistics can be used to spread misinformation online. Students will learn to recognize unsupported data or statistics and equip them with tools to fact-check data or statistics used to politicize the Student Loan Reduction Act. For more information and to complete the lesson, go here. (45 - 60 Mins)

A 2019 viral video of Nancy Pelosi slurring her words is one high-profile example of how cheap or free editing software and apps make it easy to create engaging and believable manipulated media that spread misinformation. The 2022 election cycle has had its fair share of manipulated media shared by candidates from both parties. The U.S. Senate race in Pennsylvania has produced several examples of manipulated media. Learn about the different types of manipulated media and how to spot when a video has been doctored. For more information and to complete the lesson, go here (45-60 mins)

Social media posts that share misinformation about controversial topics out of context go viral because they trigger our desire to act. Students will learn the "Top Five Ways to Check for Context" and understand why understanding the context behind the information in social media posts is important. Saying "I'm sorry" and "My bad" mean the same thing - unless you are at a funeral. For more information and to complete the lesson go here (45-6O minutes)

Fake social media generator websites have made it easy to impersonate a person or business and spread misinformation. These fake social media screenshots look like the real McCoy, but there are a few clues that can help identify fakes if you find yourself asking "Will the real Slim Shady please stand up?" while on social media. Students will learn four clues that a social media screenshot might be fake and use some online tools that can help verify them - and then put those skills to the test. For more information and to complete the lesson go here (45 mins).

Photos used out of context are the most common type of low-tech technique used to spread misinformation because we are more likely to believe information with visual imagery. Students will learn to recognize cues that images on social media should be checked and learn how to check images to make sure they accurately reflect the information in a social media post. For more information and to complete the lesson go here (45 -60 mins)

The political and social division sowed by conspiracy theories can make holiday gatherings with extended family and friends uncomfortable at best - and tear apart families at worst. In this lesson, students will learn what a conspiracy theory is, how people fall for them, and how to talk with family members and friends with differing views without it becoming confrontational. For more information and to complete the lesson go here (45 mins).

A 2022 Harris Poll by Google Cloud found that 72% of business executives in North America believe their companies have practiced "greenwashing" to look more climate-friendly than they really are - using misinformation to fool climate-conscious consumers. Students will learn to recognize "greenwashing", explain why businesses use the misleading practice, and why it is important to be an informed consumer. For more information and to complete the lesson go here (60 mins)

Performative social media activism is activism that evokes strong emotions in people eager to share messages - and sometimes misinformation - that match their beliefs around politically-charged issues. In this lesson, students will evaluate and consider bias in online content that is being used for personal gain or popularity as opposed to real support for the issue here. (45 mins)

The topic of climate change is still hotly debated online, and climate-change misinformation can spread like wildfire on social media. Students will learn how to evaluate the source of climate change posts on social media and analyze search engine results to choose the best sources to verify or debunk them here. (45 mins)

People often turn to conspiracy theories to help explain mysterious events or circumstances to make sense of their world. But there are other reasons people with good intentions believe them. Students will learn why people become susceptible to conspiracy theories and how to fact-check them here. (45 mins)

Quoting someone out of context is commonly used to spread misinformation with potentially serious consequences, such as provoking violence or providing fake evidence to spread conspiracy theories. Students will learn how quotes about the 9/11 attacks have been manipulated to spread conspiracies and read upstream to find the original sources to explain how the meaning changes here (45 mins)

Seeing is believing-which is why manipulated video can be a powerful tool to spread misinformation. Students will learn three ways that video is manipulated to spread misinformation, how to detect manipulated video, and how to fact-check it before sharing it here (45 min)

Instagram's so-called "fact" pages feature unbelievable stories that catch your attention and distract your critical thinking powers - and they often contain misinformation. Students will learn to identify these types of sites on social media, understand why it is important to double-check them, and how to fact-check them here (45 min)

Credible sources can also spread misinformation when not all the facts are clear, especially during a scary event such as the train derailment in East Palestine, Ohio. Students will explore an example of a credible source spreading misinformation about acid rain and then apply those media literacy skills to research another issue surrounding the release of dangerous chemicals here (45 min)

How to short-circuit misinformation online-media literacy final assessment

Misinformation is all over social media - and it's our job to sort fact from fiction to put a stop to it. That's why we've brought you this "Be MediaWise" curriculum. This video is the final episode of the series and will highlight some of the skills we've taught over the year-and give students some activities to practice what they've learned (60 mins)

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Through a partnership with the MediaWise Teen Fact-Checking Network and with support from Google, millions of teachers throughout the United States can access weekly lesson plans published by the PBS NewsHour Student Reporting Labs that teach specific online fact-checking skills that will be posted right here!

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If only all media could pledge their truthiness...

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The Word - Truthiness - The Colbert Report | Comedy Central US

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Stephen promises to always feel the news at his viewers.

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How limited is your access to news?

Where do most Americans get their news? Identifying the news platforms people use (e.g., TV or print) is helpful, but it's not the only important thing to know. Another critical question is how many different sources do people use within these platforms? This is significant because people tend to gravitate to a few sources that typically echo opinions they already hold. People watch just one news channel, listen to just one radio station, or read just one newspaper because the information provided conforms to their existing biases.

Social media exaggerates this situation because the algorithms that drive new posts to individuals' feeds are designed to keep those users on sites as long as possible. This is accomplished by showing users new content that has been selected just for them. As a result, people end up in an information echo chamber where what they see is heavily slanted toward what they already agree with. It's up to consumers to break out of these echo chambers to learn about other points of view.

In this activity, students work in teams to examine their personal information echo chambers and create a plan to bring more balance to this aspect of their lives.

The Assessing Words Instructional Activities are continually reviewed. If you use this activity, please complete a Feedback Form afterward.

Standards: CCSS.ELA-LITERACY.RI.6.7-6.9, CCSS.ELA-LITERACY.SL.6.1-6.4, CCSS.ELA-LITERACY.W.6.7, CCSS.ELA-LITERACY.RI.7.8-7.9, CCSS.ELA-LITERACY.SL.7.1-7.4, CCSS.ELA-LITERACY.W.7.7, CCSS.ELA-LITERACY.RI.8.7-8.9, CCSS.ELA-LITERACY.SL.8.1-8.4,CCSS.ELA-LITERACY.W.8.7, CCSS.ELA-LITERACY.WHST.6-8.7

Objective: Students will examine the sources of news they use and create a plan to insure there is balance in the information they view, read, and hear.

Student Resources

This site is meant to be used by teachers. Student Resources are provided, but please do not direct students here. Copy and paste the link to the handout and whichever of the Online Resources you want students to use on your classroom webpage, in your classroom area of the school's Learning Management System, or wherever you distribute digital materials to students.

Susan Brooks-Young: sjbrooksyoung@gmail.com

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How limited is your access to news? Where do most Americans get their news? Identifying the news platforms people use (e.g., TV or print) is helpful, but it's not the only important thing to know.

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イーライ・パリザー：危険なインターネット上の「フィルターに囲まれた世界」

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インターネット企業が私たちの好みに合わせてサービス（ニュースや検索など）をカスタマイズしようと努めるにしたがい、思いもよらぬ害が出てきています。私たちは「フィルターに囲まれた世界」に閉じ込められ、自己の世界観に疑問を投げかけたり視野を広める情報に触れる機会を失っているのです。結果的に私たちのためにならず、民主主義にも悪影響を及ぼすことになるとイーライ・パリザーが強く訴えます。

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The internet is facing a major issue. One that has a major impact on politics and belief systems in the United States, and has already divided our country politically. The issue is filter bubbles.
Contents
What is a Filter Bubble?

A filter bubble is the result of algorithm driven news feeds and search results. You may be experiencing a filter bubble every time you scroll through your News Feed on Facebook or search on Google. These sites determine which content you're most likely to engage with rather than the most accurate or complete source of information.

Filter bubbles refer to the state of intellectual isolation that can result from personalized searches on search engines or social media sites. As a result, users become separated from most information that presents an opposing viewpoint. These bubbles are associated with search engines and social media websites.

How Are Filter Bubbles Created?

Algorithmic websites, like many search engines and social media sites, show users content based on their past behavior. Depending on what you've clicked on in the past, the website shows you what it thinks you are most likely to engage with.

Social Media companies, like Facebook, want you to keep using the product. So instead of being a feed of all the information, Facebook is selective with what it puts in your feed. People often assume that the information they see is unbiased, when it is actually skewed towards their beliefs.

Why are Filter Bubbles Bad?

After awhile of only seeing results they agree with, people begin to believe that they are more correct and then their views are strengthened and solidified. This means that when someone disagrees with them, both of their views are likely to be more polarized. As a result, these people are less likely to agree with each other, or even talk to each other.

Isolation

Filter bubbles are a kind of " intellectual isolation ". This isolation creates ignorance to other perspectives and opinions. The internet was once a way of connecting with other people with different views. But with the introduction of algorithm-based feeds of information, we've broken the internet to isolate ourselves from views different from our own.

Eli Pariser, an internet advocate, said in a TED talk, "instead of a balanced information diet, we may end up with only information junk food." So, the internet, which appears at first to be a window into the world's information, may only be showing you half of the story. This is also bad for democracy, which requires political discourse and discussion of issues to reach a compromise. If everyone's views are so polarized that this becomes impossible, we're in trouble as a country.

Social Media is One Giant Filter Bubble

Wired reported that "according to Pew Research, 61 percent of millennials use Facebook as their primary source for news about politics and government." And this just shows how big of an issue that these skewed sources of information really are. Over half of millennials let Facebook's News Feed algorithm determine which news and information they see. Not only does this create a huge responsibility on Facebook's part, but also it means that young people are growing more and more isolated in their views. People's political views are constantly reinforced on social media to the point where any discourse with people with other leanings becomes impossible.

Facebook isn't the only source of biased or skewed information on social media. People fall into the same traps with other social networking sites. Twitter, for example, has the same issues. Twitter users tend to only follow people and news sources that present perspectives they agree with. No matter which apps people choose, or what news sites they rely on it's difficult to truly see the full range of information anywhere on the internet.

The Surveillance Economy Drives Filter Bubbles

Since filter bubbles are caused by algorithms, it's actually the data collection that websites use that creates the "bubbles". If internet organizations prioritized user privacy, rather than tailoring their services to each user, the isolation created by algorithms wouldn't exist. This drive to collect and monetize user data is known as " surveillance capitalism". The term was coined by Shoshana Zuboff in her most recent book. We all share some information when we go online, and it's the websites that use this information the most that are contributing to the filter bubble problem.

How to Avoid Filter Bubbles

Farnam Street's Recommendations for How To Avoid Filter Bubbles

Use ad-blocking browser extensions

Read news sites and blogs that provide a wide range of perspectives

Switch our focus from entertainment to education

Use Incognito browsing, delete search history and stay logged out if possible

Delete or block browser cookies

How Search Encrypt Avoids the Filter Bubble Effect

Private search engines are a great way to avoid filter bubbles. SearchEncrypt.com doesn't track search history in any user identifiable way. This means that results are based only on keywords. Search Encrypt 's search results aren't influenced by your political views or past internet behavior.

Personalized Internet is not Better Internet

Another way to avoid filter bubbles is by limiting the "personalized" and "tailored" aspects of the web. While it may seem like a good thing to have a custom, optimized experience, websites tracking your information and using it to determine which information you see is the exact cause of filter bubbles.
If the information you are shown is incomplete, and ads can target you specifically, is your experience really any better?
Read More:

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What Are Filter Bubbles & How To Avoid Them [Complete Guide] - Choose To Encrypt

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The internet is facing a major issue. One that has a major impact on politics and belief systems in the United States, and has already divided our country politically. The issue is filter bubbles. Contents What is a Filter Bubble? How Are Filter Bubbles Created? Why Are Filter Bubbles Bad?

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Filter Bubbles and Confirmation Bias = Fake News

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Filter Bubbles and Confirmation Bias = Fake News

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Online services like Google, Instagram, and TikTok use computer programming algorithms to determine what information to deliver to you. Your "filter bubble" (a term coined by internet activist Eli Pariser) refers to the idea that this automated personalization, though helpful in some ways, can isolate you from other information. Sometimes referred to as an "echo chamber," the filter bubble created by your online activity can limit your exposure to different points of view and weaken your ability to avoid fake news and bias.

In this now-famous TED Talk, Pariser discusses the effects of algorithms and warns us about the dangers of online filter bubbles.

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LibGuides: Fake News (and how to fight it): Filter Bubbles & Confirmation Bias

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Spotting Misinformation + Disinformation - ABC Education

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Learn some handy first steps to fact check news and information online.

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A youthful perspective on how to break through the echo chamber

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A youthful perspective on how to break through the echo chamber

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Get a daily email featuring the latest talk, plus a quick mix of trending content.

By subscribing, you understand and agree that we will store, process and manage your personal information according to our Privacy Policy

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Aditi Thayyur: Bursting the Echo Chamber: The Power of Perspective

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We are all trapped in Echo chambers... but did we know that? What is an echo chamber and why do we need to burst through?

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How Much Do Social Media Algorithms Control You? | Above the Noise | PBS LearningMedia

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The content you see on a given social media app isn’t random - it’s curated content specifically made for you. And it is curated by recommendation algorithms. These algorithms are designed to keep you on the social media app you are using for as long as possible and they do that by learning what you…

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The filter bubble is a name for an anxiety - the worry that our personalized interfaces to the Internet will end up telling us only what we want to hear, hiding everything unpleasant but important. It's a fabulous topic of debate, because it's both significant and marvelously ill-defined. But to get beyond arguing, we're going to need to actually do something. I have five proposals.

If you're not familiar with the filter bubble argument, start with Eli Pariser's TED talk. The basic idea is this: All of us now depend on algorithmic personalization and recommendation, such as Google's personalized results and the Facebook news feed which decides for us whose updates we see. But if these individually-tailored filters are successful in giving us only what we want - as measured by what we click on or "like" - then maybe they'll remove all the points of view we disagree with, all of the hard truths we'd prefer to ignore, and everything else in the world that might broaden our horizons. Stuck in our own little sycophantic universes, we'll be isolated, only dimly aware that other people exist or that we might need to work together with them in a shared world.

Or maybe not. The hyperlink has the magical ability to expose us to something completely different in just a single click. Different people have different information needs, and without algorithmic filtering systems we'd be lost in the flood of the web. And anyway, was traditional, non-personalized news really that good at diversity?

People have been talking about the dangers of personalized algorithmic filters since the dawn of the web - here's Jaron Lanier in 1995, and Cass Sunstein in 2002 - and we're still talking about it. We can order another round and argue about this forever, or we can try some new things.

1. Stop speculating and start looking

When we look at how people interact on the web, what do we actually see? It's now possible to visualize the data trails left by communities.

On Amazon, Orgnet showed that most people buy "conservative" or "liberal" books but not both by mapping the "people who read X also read Y" recommendations. On Facebook, a 2008 analysis showed that, yes, our "friends" are more likely to agree with our political attitudes than random strangers - 17 percent more likely to be exact. But it also showed that we tend to imagine our friends to be much more like us than they really are, thus inflating our perception of a filter bubble. On Twitter, people who tweet political terms break into left- and right-leaning social network clusters.

But these sorts of studies cannot answer questions of cause and effect. Do filtered media worlds cause the online segregation we see, or do people construct self-reinforcing filters because they already have divergent beliefs?

This is why the recent Facebook study of link sharing is so unusual: it's a comparative experiment to determine whether seeing a link in your Facebook news feed makes you more likely to share it. Drawing from a pool of 250 million users and 73 million URLs, Facebook researchers hid certain links from the control group, experimentally removing the effect of seeing that link on Facebook. This breaks the dashed line of causation in the diagram below, which makes it possible to estimate, by comparison, the true influence of the algorithmically customized news feed on your behavior.

The results, summarized by Farhad Manjoo at Slate, aren't a ringing validation of the filter bubble argument. Unsurprisingly, people are more likely to share links posted by close friends, where "close" is counted (algorithmically) in terms of number of recent likes, comments, etc. But because most people have many more distant friends than close friends, most of what the news feed actually influences us to share comes from weak ties, not strong ones. In other words, the news feed tends to prompt us to view and re-share information from the edges of our social network, not the center.

This study has its limitations: it tracks all URLs shared, not just "news." Maybe we're happy to share cat pictures from random folks, but we only trust close friends when it comes to political issues. Still, I'm holding it up as an example because it's both empirical (it looks at the real world) and experimental (by comparing to a control group we can determine causation.) It might be the largest media effects study ever undertaken, and if we're serious about understanding the filter bubble we need more work like this.

Simultaneously, I think we also need to be studying older forms of media. It's not enough to compare what we have now with some idealization of the past; let's really look critically at the broadcast media era to better understand the tradeoffs now being made. There's a strong argument that mainstream news only ever really represented the concerns of white, middle-class men, and of course it used to be much harder to consciously seek out alternative perspectives. But nobody ever talks about the "filter bubble of the 1960s."

2. Bring curation into journalism

Editors still command attention. Every time someone subscribes in any medium, whether that's in print or on Twitter, they are giving an editor license to direct their attention in some small way. Every time an article page includes a list of suggested stories, someone is directing attention. Editors can use this donated attention to puncture filter bubbles in ways people will appreciate.

But if there has been a decline in the power of editors to set the agenda for public discussion, maybe that's because the world has gotten a lot bigger. A news editor has always been a sort of filter, making choices to cover particular stories and selecting their placement and prominence. But they filter only the product of their own newsroom, while many others are filtering the entire web. How can users depend on a filter who ignores most of everything?

Editors could become curators, cultivating the best work from both inside and outside the newsroom. A good curator rewards us for delegating our attentional choices to them. We still like to give this job to people instead of machines, because people are smart, creative, idiosyncratic, and above all personal. We can form a relationship with a good curator, sometimes even a two-way relationship when we can use social networks to start a conversation with them at any moment.

But traditional journalism isn't really in this game. For a start, curation simply wasn't possible in broadcast and print, because those media don't have hyperlinks. News organizations tied to those media have been very slow to understand and embrace links and linking (see this, this, and this). Meanwhile, the classic "link roundup" continues to thrive as online form, social media has created a new class of curation stars such as Maria Popova and Andy Carvin, and there are hugely popular news sources that mostly curate ( Buzzfeed) or only curate ( BreakingNews).

There are many possible reasons why linking and curation have not been more fully adopted by traditional news organizations, but at heart I suspect it boils down to cultural issues and anxieties about authorship . There are glorious exceptions, such as Reuters' Counterparties, which captures what Felix Salmon and Ryan McCarthy are reading. I'd love to know what other good reporters find noteworthy; that information would be at least as valuable to me as the articles they eventually produce. I believe there's still a vital role for human "filters," but only if they're willing to direct my attention to other people's work.

3. Build better filtering algorithms

Filtering algorithms are here to stay, and we can make them better. In his book, Pariser suggests a diversity control on our news reading applications:

Alternatively, Google or Facebook could place a slider bar running from "only stuff I like" to "stuff other people like that I'll probably hate" at the top of search results and the News Feed, allowing users to set their own balance between tight personalization and a more diverse information flow.

I really like the concept of giving users simple controls over their personalized filters, but it's a monumental UI and technical challenge. We can throw around phrases like "my newsreader should show me more diverse viewpoints," but it's really hard to translate that into code, because we're not being very specific.

The task comes down to finding an algorithmic definition of diversity, and there are several avenues we could explore. Most recommendation systems try to maximize the chance that you'll like what you get (that is, literally "like" it, or click on it, or rate it five stars, or whatever.) This is an essentially conservative approach. Instead, a filtering algorithm could continually explore the boundaries of your interests, looking for what you didn't know you wanted. Luckily, this idea has mathematical form: We can borrow ideas from statistics and information theory and say that the algorithm should sample the space of possible items in a way that reduces uncertainty fastest.

Reddit already uses this idea in its comments filtering system, which asks users to vote items up or down. But you can't vote on comments you never see, which tends to trap voting-based filtering systems in a popularity feedback loop. In 2009, Reddit found a better answer: take into account the number of people who have actually laid eyes on the comment, and "treat the vote count as a statistical sampling of a hypothetical full vote by everyone, much as in an opinion poll," as Randall Munroe of xkcd fame explains ( with pictures!) What's really going on here is that the filtering algorithm takes into account what it doesn't yet know about its audience, and tries to find out quickly; the math is here.

Another possibility is to analyze social networks to look for alternate perspectives on whatever you're reading. If people in our personal social network all hold similar opinions, our filters could trawl for what people are reading outside of our home cluster, retrieving items which match our interests but aren't on our social horizon. I suspect such an algorithm could be built from a mashup of document similarity and cluster analysis techniques.

There's huge scope for possible filtering algorithms. But there isn't much scope for non-engineers to experiment with them, or engineers who don't work at Google and Facebook. What I'd really like to see is an ecology of custom filters, a thriving marketplace for different ways of selecting what we see. Then we could curate filtering algorithms just as we curate sources! This idea seems to have been most fully articulated by digital humanities scholar Dan Cohen in his PressForward platform.

4. Don't just filter, map

I think a lot about how to design better filters, and I always run into the same basic problem: I'm just not sure how to decide how someone's horizons should be broadened. There is far more that is important, a far greater number of issues that really matter, than one person could possibly keep up with. So how are we to make the choice of what someone should see on any given day? I have no good answer to this. But I see another approach: Don't try to choose for someone else. Instead, just make the possibilities clear to them.

We have no maps of the web. We have no visceral sense of its scale and richness. The great failing of search algorithms is that they only give you what you ask for, but I want a picture of the entire discoverable universe. This is the core idea behind the Overview Project, where my team and I are building a visualization system to help investigative journalists sort though huge quantities of unstructured text documents.

So what if news readers included a map of available items, and the relationships between them? Here's what it might look like for political books (click for larger):

This map, produced by Orgnet, shows the relationship of U.S. political books to one another in August 2008, as recorded in Amazon.com sales data. An arrow means that people who bought one book also tended to by the other. It's clear that people are living in two different worlds, with only a few bridges between them.

Now imagine a "You Are Here" marker on the map, highlighting books (or articles) that you've already read. Pariser argues that curiosity is the sense that you're missing something - but we're always missing something, and we're always part of a community that is isolated from others. Let's make those truths palpable in our information consumption systems. There are many concrete ways to do this; I offer some suggestions here and here.

In physical space, we can stare at a map of the world and find ourselves on it. We can tap our finger on where we came from, and realize how much we have yet to see. It is this experience that I want to replicate online.

5. Figure out what we really want

The filter bubble is a pretty abstract concept. It needs concrete examples for illustration, but pretty much all of the examples offered so far have come down a concern that the American left and right will become increasingly isolated from one another, unable to work together to solve common problems.

This is a concern, because American politicians and public alike really have become more divisive and polarized over the last several decades. But if the goal is less polarization, the filter bubble is several steps removed. Addressing polarization by addressing the filter bubble is a plan that depends on a lot of big assumptions: You have to believe that the filter bubble causes or enables social segregation by politics, that exposing people to content from alternate political viewpoints will reduce political extremism, and that there isn't some other, more direct or effective way we could deal with polarization. For example, our personal relationships change us far more than any "information" we consume, so maybe we should be talking about connecting people, not content. If we want to address political polarization, then we should start asking questions at the beginning, rather than immediately assuming that filter bubbles are the issue.

Conversely, the filter bubble concept seems like it should apply to a lot more than American politics. Pariser might imagine that a good filter gives a nice balance of liberal and conservative views, but what about more unorthodox philosophies? What about things that aren't politics at all? Maybe a diverse filter should tell me about the environmental effects of bees, or the innovations of Polish cinema. For that matter, I haven't heard anyone mention language bubbles, which are far more pervasive and invisible. Why don't my filters show me more material that was translated from Chinese? In a global era, exposing different countries and cultures to each other might ultimately be a far more important goal.

These two questions have been in the background of the filter bubble discussion, but they should be central. First, what is the scope of "diversity"? Does it mean more than domestic political attitudes? Are domestic politics even the example we should be worrying about most? Second, what is it that we are trying to accomplish? How would we know if we were successful? Why do we believe that changing our filters is the best way forward? If we can't answer these questions, then we have no basis to create better filters.
Girl-in-the-bubble photo by Mike Renlund used under a Creative Commons license.

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Are we stuck in filter bubbles? Here are five potential paths out

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Algorithms can help, but more fundamentally, we need to figure out what we want a diverse pool of information to look like.