Fake News Conversation Network in Twitter: User Type, Emotional Appeals and Motives in Network Formation
Keywords:fake news, social network analysis, LIWC, sentiment analysis, twitter
Abstract“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.
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