Defining Fake News: Features and Tricks

“Fake news” (noun) /ˌfeɪk ˈnjuːz/

‘Disinformation’ has become a well-known keyword in politics, journalism and society, hence it should be considered a complex and multifaceted phenomenon. Disinformation and ‘fake news’ have a striking impact on modern society, which is reflected in both the social and online spheres. Fake news emerges and keeps spreading as part of structured disinformation campaigns and their use is systematic regardless of the country. Approaching this topic requires keeping in mind a basic assumption: the web provides modern society with an incredibly large amount of data which people use at an even higher rate. This condition makes the perfect environment to encourage the spread of inaccurate or false information. By the end of the present article, it will be made clear that disinformation and fake news can have a political and social impact, especially due to their tendency to go viral with great rapidity. The interaction between technological and societal vulnerabilities, together with individual cognitive shortcomings, creates what Barojan calls a "toxic information environment," which facilitates the spread of disinformation (Jayakumar et al., 2021, p. 61).

Fake news spreads easily through the mask of social media.

The term ‘fake news’ is not new to today’s people. It is used to refer to viral posts based on fictitious accounts, which are precisely made to look real. Allcott and Gentzkow (2017) define fake news as “news articles that are intentionally and verifiably false and could mislead readers” (p. 213). Researchers need to face the problem of ambiguity when defining fake news: the difficulty of defining it also matches with a scarcity of statistical data which allow a systematic and detailed analysis of the phenomenon. Fake news undermines public safety and trust in government, as well as democracy itself, together with journalism and economies. During the 2016 U.S. election, fake news generated almost nine million shares, reactions and comments on Facebook about false election stories, namely outperforming the total of interactions of the nineteen major news websites (Silverman, 2016). According to Madrigal (2017), after 2016 the media world should be reconsidered "in light of Donald Trump's surprising win as well as the continuing questions about the role that misinformation and disinformation played in his election." Madrigal sustains that "The informational underpinnings of democracy have eroded, and no one has explained precisely how," meaning that Facebook constitutes a powerful, non-neutral force in electoral politics, as also shown in the study by Bond et al. (2012). The mentioned erosion of the informational underpinnings of democracy is more explicit in the article by Zittrain (2014), where the author presents a Facebook-based experiment conducted in 2010. On November 2, 2010, Facebook displayed a non-partisan "get out the vote" button on around 60 million people's newsfeeds. The experiment turned out in an estimated number of 340.000 extra voters who wouldn't otherwise have voted. According to Tandoc et al. (2017), two main reasons trigger the creation of fake news: financial and ideological matters. In fact, fake and shocking stories which go viral provide their creators with clicks — i.e. views — that can turn into revenue.

People nowadays are submerged in superficial and imprecise information.

Fake news on social media also contributes to creating "echo chambers" (Quattrociocchi et al., 2016), i.e. social media networks people rely on to support their current convictions (see also Bessi et al., 2016). Such social bubbles are based on the so called "confirmation bias," which is crucial in people's decision to spread a piece of content within their community, thus creating "homophile and polarized clusters" (Bessi, Zollo et al., 2015, p. 1). Misinformation in the media is not a recent phenomenon, as it has been part of it since the development of the earliest writing systems (Marcus, 1993). The digitalisation of news has changed the way they are produced as much as the profile of those who produce them. Online platforms allow non-journalists to reach sometimes big audiences. Thus, a phenomenon called ‘citizen journalism’ arose (Robinson and DeShano, 2011). An article published by CNN business in 2019 states that in 2018 alone Facebook shut down around 5.4 billion fake accounts — and millions of them still remained (Fung & Garcia, 2019). Consequently, the same platform in 2019 allowed its hate speech algorithms to automatically remove content that violates the company’s policy (O'Sullivan, 2019). Fake news is not mandatorily 100% false: it is still unclear indeed whether falsity is a necessary condition to define them. Fake news aims at being intentionally misleading, but there is no consensus on how such deception is conducted. Deception is, nonetheless, one pivotal feature in the definition of fake news.

Words used in fake news can alter and limit people’s thoughts.

Fake news misleads its audience in a broad sense, as words have considerable power in shaping people’s beliefs and opinions. The attempts to automate the detection of fake news (e.g. O'Brien et al., 2018, Yang et al., 2019, Rashkin et al., 2017, or Zhou et al., 2019), through the analysis of its language (see Oshikawa et. al, 2020 for a complete survey) show a particular focus on natural language processing (NLP). According to Kang et al. (2020), NLP is "a computer-assisted analytical technique aimed at automatically analyzing and comprehending human language." Such techniques are used nowadays to extract significant information from linguistic data (e.g., for stance detecting or sentiment analysis). The language used in fake news exhibits particular characteristics, that tend to recur systematically. Fake news is commonly sensational, especially if it deals with a controversial societal topic. The first aim of fake news is to provoke and elicit strong emotional reactions: this is also due to the human preference for sensational news, which is easier to remember (Davis&McLeod, 2003). The main reason for that relies upon the fact that truth is usually more boring than sensationalism, whereas the latter sells much better. Moreover, typical fake news tends to discredit other sources of information since they present themselves as the most accurate and reliable source (Denaux&Gómez Pérez, 2020). Nonetheless, this claim of validity above others does not revolve around actual proof or evidence. Another peculiar feature which distinguishes fake news is that it usually discredits other sources by underlining the presence of alleged biases or interests or the fact that such sources lied before. The goal, in this case, is clear: undermining the source's validity by insinuating doubts in the average reader. Accordingly, the language that is used in fake news is usually colloquial and simple and tends to reduce complex matters to simple problems. Therefore, this strategy appeals to the readers’ emotions to compel them to feel deeply involved in the issue. Therefore, if emotional involvement and simple solutions mix, they end up producing the engagement these contents want to reach. An additional and effective trick that is commonly found in fake news is depicting experts as ‘the élite’, thus not trustworthy. The use of a general and the underspecified target itself, such as the élite or ‘the strong powers’, is one main feature of fake news as well. Using this kind of target makes a useful trick to create an ambiguous enemy against whom the readers’ emotional reactions are directed.

A sceptical attitude is a key element to avoid getting lost in fake news.

In conclusion, useful advice for readers is to keep a sceptical attitude when presented with articles or social media posts which show the aforementioned features. Remarkably, the audience itself represents a fundamental element in the definition of fake news. If readers do not perceive fake news as real, they will not start believing something that is not true. The audience participates in the creation of fake news which in turn would remain pure fiction if not perceived as real. The process of perceiving a fake as real also enables fake news to undermine journalism’s legitimacy, especially in a social media environment (Kang et al., 2016). Well-produced fake news embodies real news in many ways: the look of a website, the style and language of an article or the attributions included in a photo. Fake news can create a real network of fake websites and has real consequences on reality, thus making it a crucial subject for further studies.

Bibliographical references

Allcott, H., & Gentzkow, M. (2017). Social Media and Fake News in the 2016 Election. Journal of Economic Perspectives, 31(2), 211–236. Barojan, D. (2021). Building Digital Resilience Ahead of Elections and Beyond. In Jayakumar, S., Ang, B., & Anwar, N. D. (Eds). Disinformation and Fake News (61-74). Palgrave Macmillan. Bessi, A., Coletto, M., Davidescu, G. A., Scala, A., Caldarelli, G., & Quattrociocchi, W. (2015). Science vs Conspiracy: Collective Narratives in the Age of Misinformation. PLOS ONE, 10(2), e0118093. Bessi, A., Zollo, F., del Vicario, M., Scala, A., Caldarelli, G., & Quattrociocchi, W. (2015). Trend of Narratives in the Age of Misinformation. PLOS ONE, 10(8), e0134641. Bond, R. M., Fariss, C. J., Jones, J. J., Kramer, A. D. I., Marlow, C., Settle, J. E., & Fowler, J. H. (2012). A 61-million-person experiment in social influence and political mobilization. Nature, 489(7415), 295–298. Davis, H., & McLeod, S. (2003). Why humans value sensational news. Evolution and Human Behavior, 24(3), 208–216. Denaux, R., & Gómez Pérez, J. M. (2020). The language of ‘fake news’. How and why does it work? Coinform.Eu. Fung, B., & Garcia, A. (2019, November 13). Facebook has shut down 5.4 billion fake accounts this year, but millions likely remain. CNN. Jayakumar, S., Ang, B., & Anwar, N. D. (2020). Disinformation and Fake News (1st ed. 2021 ed.). Palgrave Macmillan. Kang, H., Bae, K., Zhang, S., & Sundar, S. S. (2011). Source Cues in Online News: Is the Proximate Source More Powerful than Distal Sources? Journalism & Mass Communication Quarterly, 88(4), 719–736. Kang, Y., Cai, Z., Tan, C. W., Huang, Q., & Liu, H. (2020). Natural language processing (NLP) in management research: A literature review. Journal of Management Analytics, 7(2), 139–172. Madrigal, A. C. (2017, November 16). What Facebook Did to American Democracy. The Atlantic. Marcus, J. (1993). Mesoamerican Writing Systems: Propaganda, Myth, and History in Four Ancient Civilizations (First ed.). Princeton University Press. O’Brien, N., Latessa, S., Evangelopoulos, G., & Boix, X. (2018). The language of fake news: Opening the black-box of deep learning based detectors. Center for Brains, Minds and Machines. Oshikawa, R., Qian, J., & Wang, W. Y. (2018). A survey on natural language processing for fake news detection. O’Sullivan, D., CNN Business. (2019, March 28). Facebook bans white nationalism two weeks after New Zealand attack. CNN. Quattrociocchi, W., Scala, A., & Sunstein, C. R. (2016). Echo Chambers on Facebook. SSRN Electronic Journal. Rashkin, H., Choi, E., Jang, J. Y., Volkova, S., & Choi, Y. (2017, September). Truth of varying shades: Analyzing language in fake news and political fact-checking. In Proceedings of the 2017 conference on empirical methods in natural language processing (pp. 2931-2937). Association for Computational Linguistics. Robinson, S., & DeShano, C. (2011). ‘Anyone can know’: Citizen journalism and the interpretive community of the mainstream press. Journalism, 12(8), 963–982. Silverman, C. (2016, November 16). This Analysis Shows How Viral Fake Election News Stories Outperformed Real News On Facebook. BuzzFeed News. Spicer, R. N. (2018). Free Speech and False Speech: Political Deception and Its Legal Limits (Or Lack Thereof). Palgrave Macmillan. Tandoc, E. C., Lim, Z. W., & Ling, R. (2017). Defining “Fake News.” Digital Journalism, 6(2), 137–153. Yang, K. C., Niven, T., & Kao, H. Y. (2019). Fake news detection as natural language inference. Zhou, X., Zafarani, R., Shu, K., & Liu, H. (2019). Fake News. Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining. Zittrain, J. (2014, June 2). Facebook Could Decide an Election Without Anyone Ever Finding Out. The New Republic.

Visual references

Figure 1. Waters, G. (2018). Fake news illustration. [Digital illustration]. Retrieved from Figure 2. Britigan, B. (2020). Fake news and Media Literacy. [Digital illustration]. Behance. Retrieved from Figure 3. Unknown. (2020). [Hybrid art]. Retrieved from Figure 4. Unknown. (2020). [Digital illustration]. Retrieved from

Author Photo

Antonio Verolino

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