Fake News Conversation Network in Twitter: User Type, Emotional Appeals and Motives in Network Formation
Keywords:
fake news, social network analysis, LIWC, sentiment analysis, twitterAbstract
“Fake news” is nothing new but a type of yellow journalism. Since 2016 US presidential election, people became more concerned about the spread of fake news on the internet. Hashtag “fakenews” (#fakenews), in particular, has become one of the trending issues among social media conversationalists. The aim of this paper was to conduct a rhetorical investigation on the underlying motives (i.e., affiliation, achievement, power, reward, and risk) and sentiments (i.e, positive and negative) of messages containing #fakenews in Twitter. The paper also examined how the underlying sentiments and motives of such conversations are different from those of other general conversations on Twitter. Using NodeXL 11072 tweets, results analyzed via the Linguistic Inquiry and Word Count (LIWC) program showed all motives and sentiments differed significantly from the LIWC norms for Twitter text. All motives (except risk) were below the LIWC Twitter norms, suggesting that #fakenews conversations were driven by risk-focus, an overarching dimension that referred to dangers, concerns, and things to avoid (Pennebaker, Boyd, Jordan, & Blackburn, 2015), more frequently than other general conversations in Twitter. In addition, negative emotions were expressed more frequently in such conversations. Insights and results from this study will significantly add value to the current continuing scholarly and practical works on the audience’s reactions and concerns regarding the deflation of yellow journalism.References
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Balmas, M. (2014). When fake news becomes real: Combined exposure to multiple news sources and political attitudes of inefficacy, alienation, and cynicism. Communication Research, 41(3), 430-454.
Berkowitz, D., & Schwartz, D. A. (2016). Miley, CNN and The Onion: When fake news becomes realer than real. Journalism Practice, 10(1), 1-17.
Cabanac, M. (2002). What is emotion?. Behavioural Processes, 60(2), 69-83.
Cha, M., Haddadi, H., Benevenuto, F., & Gummadi, P. K. (2010). Measuring user influence in Twitter: The million follower fallacy. Icwsm, 10(10-17), 30.
Dorf, M. C., & Tarrow, S. (2017). Stings and Scams:‘Fake News,’the First Amendment, and the New Activist Journalism.
Gobet, F. (2015). Understanding expertise: A multi-disciplinary approach. Palgrave Macmillan.
Hanneman, R A., & Riddle, M. (2005). Introduction to social network methods. Riverside, CA:
University of California, Riverside. Retrieved from http://faculty.ucr.edu/~hanneman/nettext/index.html
Himelboim, I., Smith, M. A., Rainie, L., Shneiderman, B., & Espina, C. (2017). Classifying Twitter Topic-Networks Using Social Network Analysis. Social Media+ Society, 3(1), 2056305117691545.
Kadushin, C. (2012). Understanding social networks: Theories, concepts, and findings. New York City: Oxford University Press.
Kang, C., & Goldman, A. (2016). In Washington pizzeria attack, fake news brought real guns. The New York Times, 5.
Kucharski, A. (2016). Post-truth: Study epidemiology of fake news. Nature, 540(7634), 525-525.
Lee, S. K., Kim, H., & Piercy, C. W. (2016). The Role of Status Differentials and Homophily in the Formation of Social Support Networks of a Voluntary Organization. Communication Research, 0093650216641501.
Liu, B. F., Austin, L., & Jin, Y. (2011). How publics respond to crisis communication strategies: The interplay of information form and source. Public Relations Review, 37(4), 345-353.
McPherson, M., Smith-Lovin, L., & Cook, J. M. (2001). Birds of a feather: Homophily in social networks. Annual Review of Sociology, 27(1), 415-444.
McClelland, D. C. (1975). Power: The inner experience. Irvington.
Mele, N., Lazer, D., Baum, M., Grinberg, N., Friedland, L., Joseph, K., ... & Mattsson, C. (2017). Combating Fake News: An Agenda for Research and Action.
Miranda, S. (n.d.). Social Analytics. Unpublished book.
Myers, D. G. (2004). Theories of Emotion. Psychology: Seventh Edition. New York, NY: Worth Publishers.
Newman, M. E. (2006). Modularity and community structure in networks. Proceedings of the National Academy of Sciences, 103(23), 8577-8582.
Pennebaker, J. W., Boyd, R. L., Jordan, K., & Blackburn, K. (2015). The development and psychometric properties of LIWC2015.
Pew Research Center (2016). Many Americans believe fake news is showing confusion. Retrieved from http://assets.pewresearch.org/wp- content/uploads/sites/13/2016/12/14154753/PJ_2016.12.15_fake-news_FINAL.pdf
Smith, M., Milic-Frayling, N., Shneiderman, B., Mendes Rodrigues, E., Leskovec, J., & Dunne, C. (2010). NodeXL: a free and open network overview, discovery and exploration add-in for Excel 2007/2010.
Tausczik, Y. R., & Pennebaker, J. W. (2010). The psychological meaning of words: LIWC and computerized text analysis methods. Journal of Language and Social Psychology, 29(1), 24-54.
Wasike, B. S. (2003). Framing news in 140 characters: How social media editors frame the news and interact with audiences via twitter. Global Media Journal, 6(1), 5-23.
Xu, W. W., Park, J. Y., Kim, J. Y., & Park, H. W. (2016). Networked cultural diffusion and creation on YouTube: an analysis of YouTube memes. Journal of Broadcasting & Electronic Media, 60(1), 104-122.
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2021-05-28
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