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Fake News: a Concept Explication and Taxonomy of Online News Fake News: A Concept Explication and Taxonomy of Online News Maria D. Molina & S. Shyam Sundar {[email protected], [email protected]} Media Effects Research Laboratory Donald P. Bellisario College of Communications PENN STATE UNIVERSITY, USA Paper accepted for presentation in the Newspaper and Online News Division at the annual conference of the Association for Education in Journalism and Mass Communication (AEJMC) Washington, DC, August 6-9, 2018. Abstract- The growth of fake news online has created a need for computational models to automatically detect it. For such models to be successful, it is essential to clearly define fake news and differentiate it from other forms of news. We conducted a concept explication, yielding a taxonomy of online news that identifies specific features for use by machine learning algorithms to reliably classify fake news, real news, commentary, satire, and other related types of content. ake news has received serious attention Given the enormity of the fake news problem, in a variety of fields, with scholars machine-based solutions seem inevitable for F investigating the antecedents, tackling the scope and speed with which fake characteristics and consequences of its creation news is created and disseminated, especially and dissemination. Some are primarily interested around the time of elections, disasters, crises and in the nature of misinformation contained in fake other developing stories. However, in order to news, so that we can better detect fake news and develop reliable algorithms for detecting fake distinguish it from real news. Others focus on the news, we have to be very disciplined in defining susceptibility of users— why we fall for fake fake news and differentiating it from legitimate news and how we can protect ourselves from this news. vulnerability. Both are geared toward improving With this goal in mind, we launched a concept media literacy to protect consumers from false explication (Chaffee, 1991) to uncover the information. different theoretical and operational definitions Websites like Snopes and Politifact explicitly of fake news and its associated terms (e.g., address the issue by verifying information in the misinformation, disinformation, alternative facts) news cycle with the help of an army of human as described by academic research, media fact-checkers. However, human fact- checking articles, trade journals, and other relevant can be time consuming and subject to human sources. Through a meaning analysis, a taxonomy foibles such as subjectivity and being limited by of online content was developed with two prior experiences (Vorhies, 2017). An alternative primary objectives. First, through this exercise, that has been proposed is the use of machine we can pinpoint the key defining characteristics algorithms to facilitate fake news detection of fake news. Knowing the main ingredients of (Conroy, Rubin, & Chen, 2015; Wang, 2017). fake news will facilitate the development of an algorithm for detection of this type of content. information via online media. Studies show that Second, we can identify other types of content they may even be preferred over traditional that is often confused with fake news but is not professional sources (Sundar & Nass, 2001). This fake news. A conceptual understanding of these is particularly troublesome given that individuals types of content will help us better distinguish find information that agrees with prior beliefs as them from fake news and rule them out for more credible and reliable, creating an machine-detection purposes. environment that exacerbates misinformation In this paper, we will first describe the because credible information appears alongside different definitions of fake news encountered personal opinions (Bode & Vraga, 2015). through our meaning analysis and the current inconsistencies in the basic assumptions of what Defining Fake News is and is not fake news. We will then propose a Although we now have a seemingly simple theoretical definition of fake news and situate it dictionary definition of fake news as “false in a new taxonomy of online content, highlighting stories that appear to be news, spread on the the characteristics that help distinguish one type Internet or using other media, usually created to of content from another. Finally, from the influence political views or as a joke” (Fake developed taxonomy and characteristics, this News, 2018), determining what is and what is not paper will derive specific features or indicators fake news is rather complex. There is for use in a machine learning algorithm. considerable disagreement when it comes to determining which content should be considered Evolution of Fake News: Theoretical and “fake news” and which should be excluded. Operational Definitions The term “fake news” was first used to Although the interest in fake news spiked after describe satirical shows and publications (i.e.: the 2016 Presidential election, it is not a new Daily Show, The Onion). For creators of such phenomenon. The concept, known as content, the concept meant made-up news, with “disinformation” during the World Wars and as the pursuit of entertaining others, and not for “freak journalism” or “yellow journalism” during informing or deceiving. Some scholars claim that the Spanish war, can be traced back to 1896 satire should be left out of the “new definition of (Campbell, 2001; Crain, 2017). Yellow fake news” because it is “unlikely to be journalism was also known for publishing content misconstrued as factual” and it is not created with with no evidence and factually incorrect, often for the purpose of informing audiences (Alcott & business purposes (Samuel, 2016). In Yarros’ Gentzkow, 2017, p.214). However, others claim (1922) critique of yellow journalism, he that it should be included because although it is characterizes it as “brazen and vicious ‘faking,’ legally protected speech, it could be misconstrued and reckless disregard of decency, proportion and as telling the truth (Klein & Wueller, 2017). For taste for the sake of increased profits” (p. 410). example, in 2017 a satire site run by hoaxer As if history were repeating itself, the Christopher Blair issued an apology for making phenomenon regained attention during the 2016 their story “too real,” after many were unable to U.S. Presidential elections. However, what detect its satirical nature (Funke, 2017). makes fake news unique is the information The second disagreement when environment we currently live in, where social conceptualizing fake news is intentionality. Some media is key to dissemination of information and scholars believe that for content to be considered we no longer receive information solely from fake, the content creator must have deceitful traditional gatekeepers. Nowadays, it is not intent. For example, Alcott and Gentzkow (2017) necessary to be a journalist and work for a and Conroy, et al. (2015) argue that fake news publication to create and disseminate content should be defined as news articles that could online. Laypersons write, curate and disseminate mislead readers and are intentionally and verifiably false. This includes intentionally and the Latin American Chequeado, that follows fabricated pieces and satire sites, but excludes a similar classification system as Politifact. unintentional reporting of mistakes, rumors, It must be noted that the vast majority of conspiracy theories, and reports that are online fact checkers are based on corroboration misleading, but not necessarily false (Alcott & with a database of verified facts. While they can Gentzkow, 2017; Klein & & Wueller, 2017). be quite useful in fake stories about established Such conceptualization leaves out mainstream facts and also for training machine-learning media misreporting from scrutiny. As Mihailidis algorithms, it cannot help us determine the and Viotty (2017) explain, journalists face an veracity of new, incoming information about information environment where the economic, developing stories, as is often the case with the technological, and sociopolitical pressures are recent crop of fake news surrounding elections, combined with a need to report with speed, while disasters and mass shootings. Therefore, we need engaging audiences in the process. This tension a more comprehensive view of fake news, one creates an environment where online news media that not only checks on facts, but also linguistic become part of the problem of misinformation. characteristics of the story, its source(s) and the Despite misreporting being unintentional, it is networks involved in its online dissemination. still an instance of untrue information With this in mind, we propose an original disseminated via traditional as well as online taxonomy of online content, as a precursor to media channels. identifying signature features of fake news. Finally, a third disagreement regarding fake news has to do with its conceptualization as a Taxonomy of Online Content for Fake binary variable versus one that varies on a News Detection continuum. For example, the conceptualization of In our taxonomy, we identify nine categories fake news as exclusively satire provides a binary of online content for the purpose of algorithm- differentiation between genres. It is either based detection of fake news: real news, fake hard/mainstream news (based on real facts with news, polarized and sensationalist content, satire, the purpose of informing) or fake news (made- up misreporting, commentary, native advertising, stories with the purpose of entertaining). citizen journalism,
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