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From the article, fake news really clouds up the mind of the people specially those who are addicted to social media. As a student, learning hypertext and intertext really helps us to determine if articles or news are true or false. And as a citizen who upholds academic honesty ill promise to read and think critically to battle fake news.

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From the article, fake news really clouds up the mind of the people specially those who are addicted to social media. As a student, learning hypertext and intertext really helps us to determine if articles or news are true or false. And as a citizen who upholds academic honesty ill promise to read and think critically to battle fake news.

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Based on the article, fake news definitely continues to plague our information environment. And as I learn about hypertext and intertext I have learned the importance of discernment between fake news and legitimate news.

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Based on the article that we read, FAKE NEWS is definitely continues to plague of our information in the environment and i will do my best to be consistent with this battle my academic works properly

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Fake news continues to be a problem in our information world, according to the article. I shall continue to fight this struggle by appropriately completing my academic literary works with acceptable references.

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Abstract

As the scourge of "fake news" continues to plague our information environment, attention has turned toward devising automated solutions for detecting problematic online content. But, in order to build reliable algorithms for flagging "fake news," we will need to go beyond broad definitions of the concept and identify distinguishing features that are specific enough for machine learning. With this objective in mind, we conducted an explication of "fake news" that, as a concept, has ballooned to include more than simply false information, with partisans weaponizing it to cast aspersions on the veracity of claims made by those who are politically opposed to them. We identify seven different types of online content under the label of "fake news" (false news, polarized content, satire, misreporting, commentary, persuasive information, and citizen journalism) and contrast them with "real news" by introducing a taxonomy of operational indicators in four domains-message, source, structure, and network-that together can help disambiguate the nature of online news content.

"Fake news," or fabricated information that is patently false, has become a major phenomenon in the context of Internet-based media. It has received serious attention in a variety of fields, with scholars investigating the antecedents, characteristics, and consequences of its creation and dissemination. Some are primarily interested in the nature of misinformation contained in false news, so that we can better detect it and distinguish it from real news. Others focus on the susceptibility of users-why we fall for false news and how we can protect ourselves from this vulnerability. Both are geared toward improving media literacy to protect consumers from false information.

Websites like Snopes and Politifact explicitly address the issue by verifying information in the news cycle with the help of an army of human fact-checkers. However, human fact-checking can be time consuming and subject to human foibles such as subjectivity and being limited by prior experiences ( Vorhies, 2017). An alternative that has been proposed is the use of machine algorithms to facilitate the detection of false information ( Conroy, Rubin, & Chen, 2015; Wang, 2017). Given the enormity of the fabricated news problem, machine-based solutions seem inevitable for tackling the scope and speed with which it is created and disseminated, especially around the time of elections, disasters, crises, and other developing stories. However, in order to develop reliable algorithms for detecting fabricated news, we have to be very disciplined in defining it and differentiating it from legitimate news.

Several scholars have led the way in defining and classifying "fake news." Southwell, Thorson, and Sheble (2017) pinpoint conceptual differences between misinformation and disinformation, and discuss in detail the grounding of truth necessary for defining misinformation. Lazer et al. (2018) reminds us of the importance of process and intent when conceptualizing "fake news," while Jack (2017) further disentangles the conceptual differences and similarities among an array of concepts associated with problematic information, ranging from disinformation to propaganda. Tandoc, Lim, and Ling (2018), on the other hand, analyze how the term "fake news" specifically has been used by scholars, and developed a typology based on facticity and intention to deceive. They proceeded to place different kinds of online content on these two dimensions, with Propaganda, for example, scoring high on both, whereas fabrication is low on facticity but high on deceptive intention and satire being high on facticity and low on intention to deceive. Such classification of different types of "fake news" is a useful starting point in enhancing our understanding of the phenomenon. However, we need more such distinguishing characteristics and dimensions, especially those that can be usefully incorporated in automated detection algorithms. Facticity is useful for fact-checking news stories, but cannot be relied on in the case of breaking news about emergent events where no previous information is available. The intent to deceive may probably be inferred by knowing the pedigree of the news source, but can be difficult to establish in a dispositive manner. Thus, for machine learning purposes, we need a more comprehensive definition that can not only distinguish between a variety of "fake news" but also lend itself to operationalization at a very granular level for machine detection.

Previous research has provided conceptual differences and similarities between the many terms associated with "fake news." The purpose of the current research is to go one step further by identifying specific operational features or indicators that can be fed into a machine-learning algorithm to reliably differentiate between different types of content that are associated with the broad label of "fake news." With supervised machine learning, the goal is to develop "algorithms that reason from externally supplied label training instances to produce general hypotheses, which then make predictions about future instances" ( Kotsiantis, 2007, p. 249). Through this process, the machine is trained with preestablished indicators, or features, from a training set, to then predict its corresponding category for unseen instances by a set of learned rules. For example, Decision Tree algorithm learns a sequence of yes/no questions conditioned on input features to make classification, or Naïve Bayes family of algorithms models the conditional probability of different "fake news" categories given input features to make predictions. In this process, the best fitting algorithm is selected and its performance metric (e.g., Accuracy, F1 score) is calculated ( Murphy, 2012). Moreover, these operational features are interpretable, which is more beneficial than latent features automatically learned by powerful deep learning models that are usually obscure. Classification works through a set of indicators and results in a set of rules (e.g., Decision Tree), meaning that no one rule serves as ground truth for a classification, and overlapping indicators can exist between categories. Importantly, indicators need to be operationalized to a sufficiently concrete degree, a process called features engineering, in order to allow the algorithm to perform its prediction. The goal of this research, thus, is to identify novel indicators or rules that can be used by a machine-learning algorithm to predict if a piece of information is indeed one among the types of content that we label as "fake news." Once they are successfully engineered into algorithms, these indicators can be used in literacy programs to help online users be more attentive to their information environment, providing specific tips that could guide them in identifying if a piece of online content is indeed legitimate news or "fake news."

A complicating factor in this exercise is that "fake news" no longer refers simply to false information. As Vosoughi, Roy, and Aral (2018) point out, the term "fake news" has been "irredeemably polarized" in that it has been co-opted by politicians to refer to any information put out by sources that do not support their partisan positions. Waisbord (2018) calls it a "trope used by right-wing politicians, commentators and activists to castigate critical news organizations" (p. 1867). The central goal of these partisan efforts is to cast aspersions on the veracity of the content by suggesting that it is false. In many cases, such content is not even news where truth or falsehood is relevant, but a piece of commentary that expresses a particular point of view or an incomplete report of an event by a citizen journalist than can be interpreted in multiple ways. By extending the notion of falsehood to these other categories of non-news content, the term "fake news" has been widely weaponized. But, it has also resulted in the application of the label to a confusing array of content that lies at the intersection of legitimately real and patently false information, thereby posing considerable challenges to machine-based classification of "fake news."

To reduce the semantic clutter around the term "fake news" and derive meaningful indicators of the various types of content that has now become part of it, we launched a concept explication ( Chaffee, 1991) that uncovers the different theoretical and operational definitions of "fake news" and its associated terms (e.g., misinformation, disinformation, alternative facts, and false news) found in academic research, media articles, trade journals, and other relevant sources. We reviewed publications found through Google Scholar and a library database using the aforementioned keyword searches. We then followed a snowball approach to identify additional relevant articles until we reached a saturation point in terms of variety in theoretical definitions ( Saunders et al., 2018).

Through this meaning analysis, a taxonomy of online content was developed with two primary objectives. Our first objective was to pinpoint the key defining characteristics of false information. Knowing the main ingredients of false news will facilitate the development of an algorithm for detection of this type of content. Second, we wanted to identify other types of content that is often confused with false news but is not false news. A conceptual understanding of these types of content will help us better distinguish them from false news and rule them out for machine-detection purposes.

The goal of this article, therefore, lies not only in describing different types of information and misinformation that people encounter online but also in outlining specific similarities and differences between these at the operational level. We do this by first describing the different definitions of false news encountered through our meaning analysis and the current inconsistencies in the basic assumptions of what is and is not false news. We will then propose a theoretical definition of false information and situate it in a new taxonomy of online content, highlighting the characteristics that help distinguish one type of content from another. We compiled these characteristics from the literature reviewed for this concept explication and categorized them into features of content, source, structure, and network in an effort to provide guidance to algorithm designers about the appropriate loci for each type of content. Finally, from the developed taxonomy and characteristics, we derive specific features or indicators for use in a machine-learning algorithm.

Evolution of Fake or Fabricated News: Theoretical and Operational Definitions

Although the interest in "fake news" spiked after the 2016 Presidential election, it is not a new phenomenon. The concept, known as "disinformation" during the World Wars and as "freak journalism" or "yellow journalism" during the Spanish war, can be traced back to 1896 ( Campbell, 2001; Crain, 2017). Yellow journalism was also known for publishing content with no evidence and therefore factually incorrect, often for business purposes ( Samuel, 2016). In Yarros' (1922) critique of yellow journalism, he characterizes it as "brazen and vicious 'faking,' and reckless disregard of decency, proportion and taste for the sake of increased profits" (p. 410).

As if history were repeating itself, the phenomenon regained attention during the 2016 U.S. Presidential elections. However, what makes fabricated news unique is the information environment we currently live in, where social media are key to dissemination of information and we no longer receive information solely from traditional gatekeepers. Nowadays, it is not necessary to be a journalist and work for a publication to create and disseminate content online. Laypersons write, curate, and disseminate information via online media. Studies show that they may even be preferred over traditional professional sources ( Sundar & Nass, 2001). This is particularly troublesome given that individuals find information that agrees with prior beliefs as more credible and reliable, creating an environment that exacerbates misinformation because credible information appears alongside personal opinions ( Bode & Vraga, 2015).

Although we now have a seemingly simple dictionary definition of "fake news" as "false stories that appear to be news, spread on the Internet or using other media, usually created to influence political views or as a joke" ( Fake News, 2018), determining what is and what is not false is rather complex. There is considerable disagreement when it comes to determining which content should be considered "fake news" and which should be excluded. This holds especially true as the term "fake news" has become highly political and is often used as a buzzword not only used to describe fabricated information but to undermine the credibility of news organizations or argue against commentary that disagrees with our own opinion ( Nielsen & Graves, 2017; Tandoc et al., 2018). Moreover, classifying a piece of content as false requires a grounding of a universal truth, which can be a difficult endeavor that requires collective consensus (see Southwell et al., 2017). We acknowledge the complexities and ambiguity of the term. As such, we will use the term "fake news" to provide a historical background and explain the current uses of the term. We also use it as a broad umbrella term with seven categories under it, of which false information (of false news) is one of them. This is because we are interested in identifying news that is fabricated or fictitious and disseminated online. As such, we need to contain the concept so that we focus on identifying this type of problematic information instead of opening a debate that would undermine the goal of our project. We also use the umbrella term "fake news" in our taxonomy rather than the label of misinformation as suggested by scholars in the field (i.e., Southwell et al., 2017) because misinformation is by definition false information determined based on a grounding of truth and applies only to informationally oriented content. However, content that is often accused of being "fake news" is not always intended for informational purposes. For example, some of it is clearly persuasive content that is intended to persuade, not necessarily inform. Satirical accounts of news events (the original meaning of "fake news") is also not meant for information, but for entertainment. Then, there are other kinds of content that although are not "real news" per se, are not falsehoods or erroneous either, for example, commentary or citizen journalism. In other words, our taxonomy goes beyond identifying false information (whether intentional or not), to identifying the different types of content available online that might be mislabeled as false or "fake." We acknowledge that using the term "fake news" is contentious because of its highly politicized nature. Nevertheless, the goal of our project is to precisely illustrate how the term "fake news" has been used to refer to different types of content online, that in reality are different form each other, theoretically and empirically, and therefore important to include in any taxonomy.

The term "fake news" was first used to describe satirical shows and publications (i.e., Daily Show, The Onion). For creators of such content, the concept meant made-up news, with the pursuit of entertaining others, and not for informing or deceiving. The first disagreement when defining fake news is if satirical publications should be included in the definition of fabricated news. Some scholars claim that satire should be left out of the new definition of "fake news" because it is "unlikely to be misconstrued as factual" and it is not created with the purpose of informing audiences ( Allcott & Gentzkow, 2017, p. 214). However, others claim that it should be included because although it is legally protected speech, it could be misconstrued as telling the truth ( Klein & Wueller, 2017). For example, in 2017, a satire site run by hoaxer Christopher Blair issued an apology for making their story "too real," after many were unable to detect its satirical nature ( Funke, 2017).

The second disagreement when conceptualizing "fake news" is intentionality. Some scholars believe that for content to be considered fake, the content creator must have deceitful intent. For example, Allcott and Gentzkow (2017) and Conroy et al. (2015) argue that "fake news" should be defined as news articles that could mislead readers and are intentionally and verifiably false. This includes intentionally fabricated pieces and satire sites, but excludes unintentional reporting of mistakes, rumors, conspiracy theories, and reports that are misleading, but not necessarily false ( Allcott & Gentzkow, 2017; Klein & Wueller, 2017). Such conceptualization leaves out mainstream media misreporting from scrutiny. As Mihailidis and Viotty (2017) explain, journalists face an information environment where the economic, technological, and sociopolitical pressures are combined with a need to report with speed, while engaging audiences in the process. This tension creates an environment where online news media become part of the problem of misinformation. This issue is best described in Benkler, Faris, and Roberts (2018). Findings of this extensive study illustrate how major news outlets such as The New York Times, The Washington Post, and Associated Press were involved in disseminating false information. For example, according to the authors, "Russian and right-wing political actors have been particularly effective at using document dumps, particularly hacked emails, to lure journalists into over-reporting" ( Benkler et al., 2018, p. 358). Despite misreporting being unintentional, it is still an instance of untrue information disseminated via traditional as well as online media channels.

Finally, a third disagreement regarding "fake news" has to do with its conceptualization as a binary variable versus one that varies on a continuum. For example, the conceptualization of "fake news" as exclusively satire provides a binary differentiation between genres. It is either hard/mainstream news (based on real facts with the purpose of informing) or "fake news" (made-up stories with the purpose of entertaining). However, literature exploring "fake news" post-2016 election argues that "fake news" should be seen as a continuum because there is some online content that have biases, but do not fall directly into "fake news." As Potthast, Kiesel, Reinartz, Bevendorff, and Stein (2017) explain "hardly any piece of 'fake news' is entirely false, and hardly any piece of real news is flawless" (p. 4). Those who conceptualize "fake news" as a continuum include human fact-checking sites such as Snopes and Politifact that classify articles based on their degree of fakeness, rather than as absolutely true or false. For example, Politifact classifies "fake news" on a 6-point scale ranging from true to pants on fire and Snopes classifies in 12 categories including, true, false, mostly true, mostly false, mixture, misattribution, legend, and scam. Other human fact checkers include FactCheck.org, whose goal is to verify statements in transcripts and videos, and the Latin American Chequeado, that follows a similar classification system as Politifact.

It must also be noted that the vast majority of online fact checkers are based on corroboration with a database of verified facts. While they can be quite useful in identifying fabricated stories about established facts and also for training machine-learning algorithms, this method cannot help us determine the veracity of new, incoming information about developing stories, as is often the case with the recent crop of fabricated news surrounding elections, disasters, and mass shootings. Therefore, we need a more comprehensive view of fabricated news, one that not only checks on facts, but also linguistic characteristics of the story, its source(s), and the networks involved in its online dissemination. This is especially true given that when labeling and differentiating between problematic information, there is rarely a mutually exclusive characteristic because their meanings often have overlapping boundaries ( Jack, 2017). The difficulties associated with human coding in such instances can be facilitated by the use of the probabilistic functioning of machine learning, where the machine is provided with input variables x to predict output y based on a series of classifications ( Kotsiantis, 2007; Murphy, 2012).

Identifying fabricated information online before it becomes viral is imperative for maintaining an informed citizenry able to make decisions required in a healthy democracy. Using machine learning and including several types of indicators could provide a solution for the identification of fabricated information, despite the overlapping boundaries it has with other types of information. With this in mind, we propose an original taxonomy of online content, as a precursor to identifying signature features of fabricated news.

Taxonomy of Online Content for "Fake News" Detection

In our taxonomy, we identify eight categories of online content for the purpose of algorithm-based detection of "fake news:" real news, false news, polarized content, satire, misreporting, commentary, persuasive information, and citizen journalism. These categories are organized based on a combination of unique features derived and compiled from the various conceptual and operational definitions proposed for fabricated news through our meaning analysis. Such features include characteristics related to the message and its linguistic properties, its sources and intentions, structural components, and network characteristics. In the next section, we will first differentiate between real news and false news. Then, we identify online content that is not false news, but that could be misinterpreted by audience as false news. These types of online content are important to identify for the sake of building a taxonomy that has discriminant validity in ruling out content that is not false. Once identified, we can build algorithms to label the varied forms of news that exist between the binary categories of real and false, so that the reader can factor that in their consumption of such information or discourse. It will also serve to reduce reactance that is known to occur when readers are told that a piece of news which aligns well with their political beliefs is indeed false in a blanket manner. Providing a more nuanced labeling of partisan content, for example, without declaring it outright as false, can serve to balance the need for identifying content that is completely false and made-up and recognizing content in which truthfulness might be contested, as might be the case with partisan and persuasive content. This will also help enhance credibility of the algorithm and greater acceptance of its classification of different kinds of real and false news and the various shades in between the two.

Even though some content posted online may indeed be misleading, it is important to not confuse them with false news or real news. The goal of developing algorithms for fabricated news detection is not to impinge on users' right to express their opinions, or in journalists' endeavors, but to stop dissemination of false information. However, in order to achieve this goal, we need to identify the types of online content that are often misattributed to be false news or real news.

Features or Indicators of "Fake News"

Throughout this explication, initial features or indicators of "fake news" were identified based on a meaning analysis of descriptions found in academic articles, trade publications, newspaper, and magazines, among others. These features are decomposed as features of the message or linguistics, features of sources and intentions, structural features, and network features and can be useful when assessing online content based on the proposed taxonomy.

For example, through our taxonomy, we have identified an array of message features. Message quality elements such as punctuation errors or spelling mistakes are alerts of the possibility of an article to be false. Similarly, lexical components such as the word "should" or "you" can indicate that the content is possibly an opinion piece or promotional content. Visual components are also included, as a difference in pixel structure or a reverse image search can assess the realness or fakeness of an image.

Features of sources and intentions include sources within the message as well as the source of creation of the content. For instance, if an article does not attribute its sources, or these are not verified, it is a red flag or indicator that the content might be false. Likewise, features of the source include the site of origin and pedigree. An article coming from an obscure site or social media post is more likely to be false.

Structural features can also provide means of identifying type of content. Typical structural features of fabricated sites is the URL, often mimicking that of a traditional outlet, but ending in .com.co (e.g., www.abc.com.co), as well as a fairly recent date of registration. Other indicators include the "contact us" section, which provides a personal e-mail account rather than one of a reputable company.

Finally, features of the network are related to the dissemination of articles and structure of the technology that allows for this to occur. For example, false news is often shared by pre-identified accounts and our network of family and friends. Because companies like Facebook and Google keep their algorithms a trade secret, features of the network are more difficult to be identified, yet such features are important for better understanding dissemination of fabricated content.

The different features are meant to be indicators or red flags of fakeness, and each piece of online content should be analyzed in terms of all the features to determine where it belongs in our taxonomy. Importantly, these indicators or red flags are not, in and of themselves, deterministic of a type of content. Instead, the combination of several indicators can enable an algorithm to estimate the relative probability of a piece of content belonging to one of the eight categories in our taxonomy. For example, real news sometimes also has punctuation or spelling mistakes, nevertheless false information is more likely to possess these characteristics. Likewise, well-known credible news sources sometimes get facts wrong, but the likelihood of false news is higher if the source is an unknown URL. Because there are many overlapping factors, we identified different types of characteristics to assess the content in a holistic manner and calculate the likelihood of content belonging to a particular category in our taxonomy.

For example, the different features in our taxonomy can be built into sequences of yes/no questions (see e.g., in Table 9) conditioned on input features to build various supervised learning models such as Naïve Bayes, Linear Regression, or Support Vector Machines ( Cortes & Vapnik, 1995; Maron, 1961). Among many popular supervised learning models, the Decision Tree model (see Figure 1) is one of the simplest learning models, wherein it is intuitive to interpret the classification result ( Quinlan, 1986). The combination of features will indicate where in the taxonomy a particular piece of content will best belong, based on probabilistic calculations. For instance, as illustrated in Table 9, if a piece of content is not fact-checked, emotionally charged, written in narrative style, sources not verified, has inconsistencies with the registration date, and comes from an unknown source, then it is more likely to be flagged as "false news" than if the content is emotionally charged and written in narrative style, but is fact-checked, uses verified sources, comes from a known source, and does not have registration inaccuracies (in which case, it would be categorized as "commentary").

Example of Indicators of "Fake News" Converted Into Yes/No Questions for Decision Tree Algorithm.

Importantly as well, Figure 1 illustrates a Decision Tree model using the subset of features listed in Table 9, assuming that the task is to distinguish eight different articles belonging to eight different types. Despite overlapping features, the system is able to classify the eight types of content by picking specific features that help identification in each instance, to reach a conclusion. Furthermore, as is common with machine learning output, performance accuracy is calculated. This helps further assess the certainty with which we can classify a piece of content in a particular category.

This Decision Tree implementation is an example of how the proposed taxonomy can be used in machine learning detection using a subset of characteristics identified in this explication. The use of machine learning for false news detection is still at an early stage. Future study of fabricated news characteristics can expand on the list of features we have proposed and test them, as we search for distinguishing features for including in such an algorithm. Through this testing, researchers can assess the predictive power of the identified characteristics, and the combination of features that is optimal for detecting the eight types of content identified.

Note that we used the Decision Tree model to illustrate how various features that we have identified through our concept explication can be used to help machine learning models detect variants of "fake news." By following the root-to-leaf path in a Decision Tree, one can precisely "explain" why a particular article was determined to be a particular variant of "fake news" with respect to features (i.e., if-then-else rules; Quinlan, 1986). In real settings, a more advanced but complicated supervised learning model, such as the Random Forest model, is likely to outperform the Decision Tree model in its classification accuracy ( Ho, 1998). One can also use state-of-the-art Deep Learning models that are likely to improve the classification accuracy even more, but they run the risk of not being able to explain "why" it classified a piece of content in a certain category ( Ivakhnenko & Lapa, 1973; LeCun et al., 1989). Such black-box deep learning models cannot explain why they make certain internal choices across layers of the network in the way that they did. While its output accuracy often outperforms popular supervised learning models significantly, its inability to satisfactorily explain "why" or "through what process" an article is classified to be a particular variant of "fake news" may lead users or applications to prefer supervised learning models, where the choice of features from our concept explication is very important for understanding the logic of classification.

The proposed list of features can additionally be utilized to develop literacy programs where users are provided with the eight types of content proposed in our taxonomy and their delineating characteristics. These programs will serve as toolkits to use when consuming online information, allowing users to value a piece of content for what it is and not confuse an advertisement or opinion piece, for example, with real news. Identifying and labeling content as belonging to a specific category will additionally help reduce reactance that is known to occur when readers are told a piece of content is blatantly false. This is because our classification provides a more nuanced and comprehensive differentiation among types of content encountered online. For example, in Nyhan and Reifler (2010), when participants were given attitude-discrepant corrective arguments, there was a backfire effect, strengthening their belief rather than changing it. Similar effects were found in Hart and Nisbet's (2011) research investigating polarization on controversial science issues. In these studies, participants assumed that the information they are reading is news. The reality is that Americans are no better than chance at distinguishing between facts and opinion statements ( Mitchell, Gottfried, Barthel, & Sumida, 2018), nor can they differentiate news from promotional content ( Stanford History Education Group, 2016). Differentiating between these types of content was easier before the Internet. For example, opinion was a section of its own in the newspaper, and different design elements were used to signal readers that a piece of content was an opinion rather than fact ( Schallom, 2018). Similarly, with promotional content, banner ads used in traditional advertising are more likely to be differentiated due to their design, and in fact are often avoided by users ( Drèze & Hussherr, 2003). This distinction between different types of content is blurred in the current information environment where information, satire, polarized content, opinion, promotional content, and so on, are all displayed in similar formats, and the content creator can be anyone with access to the Internet. Schallom (2018) suggests that guidelines should be established at an industry level to help users differentiate between types of content. Our taxonomy will help inform these guidelines while also providing fodder for literacy programs where we not only teach people about false and real information but also provide strategies to pinpoint key differentiating elements among the different types of content.

Discussion

This article has presented a first-cut explication of the concept of false news, with the goal of uncovering its different theoretical and operational definitions. This exercise, in turn, helped identify features to aid in detecting false news through a machine-learning algorithm. Our analysis unraveled various disagreements regarding the definition of false news and what content should and should not be included. This disagreement includes issues of intent, the inclusion or exclusion of satire content, and the nature of false news as binary or a continuum.

We propose conceptualizing false news such that any content, regardless of genre (political, sports, entertainment, etc.) and intentionality, can be assessed based on characteristics of its message, structure, sources, and network. Based on our explication, we identify a taxonomy of online content with unique features that can eventually be fed into a machine-learning algorithm for false news detection. In our taxonomy, we propose an analysis where no characteristic is the sole definer for classifying a piece of content as real/false news, but rather one of many indicators. Even though the list of features is not exhaustive, it can be used as a guideline for a multipronged analysis of online content. Due to the complexity of our online environment where anyone can create and disseminate content, the boundaries between types of content are increasingly blurred. The probabilistic functioning of machine learning provides a unique tool to combat the scourge of "fake news" because it assesses content based on a series of characteristics and learned rules, thus performing mutual disambiguation for estimating the probability that it is real, false, or any one of the other subcategories of "fake news" identified in our explication. Nevertheless, this area of study is rather nascent. Features or indicators of each type of content need to be further fine-tuned, developed, and tested.

Limitations of this study include the narrow search mechanism utilized for data collection. Since our interest was in identifying conceptual and operational definitions of false news, our search terms were limited to those referring to misinformation and its associated terms. It is possible that there are other articles exploring characteristics of the different types of content we identified that we did not account for. Second, in our research we focus on false news or made-up information with the purpose of making money or creating political division. We acknowledge the concrete focus might be a limitation when considering the broad, contested, and observer-dependent "fake news" phenomenon ( Giglietto et al., 2016). Nevertheless, this concrete focus is imperative when utilizing machine learning methods of detection of such problematic content before it becomes viral. Furthermore, although we identified features of the message, source, structure, and network, the meaning analysis we performed outlined more characteristics of content than of the other three categories. As Lazer et al. (2018) explain, process and intent are arguably more important than content when identifying real versus false news because false information typically mimics the form of real information. Finally, as aforementioned, the identification of false news through machine learning is in its initial stages of development. It is important to flesh out the features to even greater level of detail than what we have specified, and test them.

Such limitations notwithstanding, this article has elucidated how the social sciences can complement computer sciences by informing the development of a machine-learning algorithm for "fake news" detection. Our research goal took the initial step to explicate "fake news" with the explicit purpose of identifying features that could be useful for machine learning. However, future research should computationally test which features work and which do not work for these purposes. For instance, it is possible that the current indicators can distinguish between certain types of content better than others. Take for example, misreporting. With the current indicators, it is likely that the algorithm could identify it once a news has been labeled as corrected, but this would be more difficult if this was not the case. Additionally, it is also important to test these features in different contexts and genres. For instance, while native advertisement might not be as prominent for political information, polarized content might be of greater importance. We also call for more content analyses of existing content belonging to each of our categories so that we can better understand their respective distinguishing features. This is especially true for citizen journalism, and promotional content, where our meaning analysis revealed fewer characteristics. We also suggest that these content analyses focus not only on content characteristics, but those of the network, source/intent, and structure.

In conclusion, we hope that the taxonomy proposed in our tables provide the foundation for identifying the major features that can aid reliable classification of "fake" and real news by both machines and humans, and thereby promote media literacy as well as a more credible information environment.

References

Benkler, Y., Faris, R., Roberts, H. (2018). Network propaganda: Manipulation, disinformation, and radicalization in American politics. New York, NY: Oxford University Press. |
Google Scholar Crossref

Cortes, C., Vapnik, V. N. (1995). Support-vector networks. Machine Learning, 20, 273-297. |
Google Scholar Crossref | ISI

Frank, R. (2015). Caveat lector: Fake news as folklore. Journal of American Folklore, 128, 315-332. |
Google Scholar Crossref | ISI

Lazer, D. M., Baum, M. A., Benkler, Y., Berinsky, A. J., Greenhill, K. M., Menczer, F., . . . Schudson, M. (2018). The science of fake news. Science, 359, 1094-1096. |
Google Scholar Crossref | Medline

Ng, Y., Zhao, X. (2018). The human alarm system for sensational news, online news headlines, and associated generic digital footprints: A uses and gratifications approach. Communication Research. Advance online publication. doi: |
Google Scholar SAGE Journals

Sheble, L. (2018). Misinformation and science: Emergence, diffusion, and persistence. In Southwell, B. G., Thorson, E. A., Sheble, L. (Eds.), Misinformation and mass audiences (pp. 157-176). Austin: University of Texas Press.
Google Scholar

Shoemaker, P. J. (2017). News values: Reciprocal effects on journalists and journalism. In Rössler, P., Hoffner, C. A., van Zoonen, L. (Eds.), The international encyclopedia of media effects (pp. 1-9). New York, NY: Wiley. doi: |
Google Scholar Crossref

Shu, K., Wang, S., Le, T., Lee, D., Liu, H. (2018). Deep headline generation for clickbait detection. In 2018 IEEE International Conference on Data Mining (ICDM), 467-476. |
Google Scholar Crossref

Yarros, V. S. (1922). Journalism, ethics, and common sense. International Journal of Ethics, 32, 410-419. |
Google Scholar Crossref

Author Biographies

Maria D. Molina is a doctoral student (ABD) in the Donald P. Bellisario College of Communications at The Pennsylvania State University. Maria's research builds upon psychology and communication theory to advance interface design and user experience of digital media technologies. Specifically, her research focuses on exploring how and why people share online and the role of technological affordances at promoting sharing behavior.

S. Shyam Sundar ( http://bellisario.psu.edu/people/individual/s.-shyam-sundar) is James P. Jimirro professor of Media Effects and founding director of the Media Effects Research Laboratory at The Pennsylvania State University. His research investigates social and psychological effects of technological affordances in interactive, digital media. He edited the first-ever Handbook on the Psychology of Communication Technology (Blackwell Wiley, 2015) and served as editor-in-chief of the Journal of Computer-Mediated Communication from 2013 to 2017.

Thai Le is pursuing his Ph.D. degree at The Pennsylvania State University. He is majoring in Information Science and Technology. Thai mainly contributes in the field of Machine Learning and AI. His research lies in the area of interpretable AI, large-scale synthetic data generation and augmentation.

Dongwon Lee is an associate professor in the College of Information Sciences and Technology (a.k.a. iSchool) of The Pennsylvania State University, USA. He researches broadly in Data Science on the management and mining of data in diverse forms including structured records, texts, graphs, social media, and Web. Further details of his research can be found at: http://pike.psu.edu/.

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"Fake News" Is Not Simply False Information: A Concept Explication and Taxonomy of Online Content - Maria D. Molina, S. Shyam Sundar, Thai Le, Dongwon Lee, 2021

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Abstract As the scourge of "fake news" continues to plague our information environment, attention has turned toward devising automated solutions for detecting problematic online content. But, in order to build reliable algorithms for flagging "fake news," we will need to go beyond broad definitions of the concept and identify distinguishing features that are specific enough for machine learning.

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1. With your knowledge about hypertext and intertext, how can you analyse this article and take your stand in the battle against “fake news”?