Gatekeeping Fake News Discourses on Mainstream Media Versus Social Media

Abstract

This study analyzes mainstream media (MSM) coverage of fake news discourse and compares it with social networking sites (SNS) users who reference the term “fakenews” in their tweets. The study employs computational methods by analyzing over 8 million tweets and 1,350 news stories using topic modeling. Building on the theory of (networked) gatekeeping and Herman and Chomsky’s propaganda model, the results show that SNS users follow networked gatekeeping practices by mostly associating fake news references to the alleged bias of MSM. On the other hand, MSM coverage tends to link fake news to SNS’s negative role in spreading misinformation. I argue here that there is a networked flak activity on Twitter which is defined as a collective negative response to MSM in order to discipline it, change its tone and editorial stance, or undermine the public’s trust in it.

The causes and consequences of COVID-19 misperceptions: Understanding the role of news and social media

“…washing hands, social distancing) appeared much more frequently in the Canadian news media. These findings are in line with literature examining fake news which finds a large difference in information quality across media (Al-Rawi, 2019;Guess & Nyhan, 2018).…”

Investigating Public Discourses Around Gender and COVID-19: a Social Media Analysis of Twitter Data

“…https://t.co/n0J5lMAU70." 5 The same observation is found upon examining the top retweets in the datasets of men and SGM. For example, and similar to the top tweets on women, we found two tweets that contained political messages: one criticized Kamala Harris, US Democratic senator, while the other one mocked Boris Johnson, the UK's Prime Minister.…”

“…1 Frequency of tweets referencing women (top), men (middle), and sexual/gender minorities (bottom) analysis (FA). The FA approach ranks topics based on the Eigenvalue that is a mathematical linear system, indicating the dominance of certain topics in the text corpus, for the higher this value is, the more dominant the topic is found in the corpora ( [5], p. 691). Both the sentiment analysis and topic modelling tools that we used did not require any pre-processing data procedures as they have them both built in.…”

“…This could also be noted from the top mentions of "abbyabrams" 6 (n=12,778). Abigail Abrams, a journalist from TIME, covered a story about an uninsured woman COVID- 5 Another example is found in the second most retweeted post which indirectly attacks the climate change activist, Greta Thunberg, stating: "RT @DeAnna4Congress: 75% of doctors agree that Hydroxychloroquine & Zithromax are an effective treatment for #COVID19. The Media: UNPROVEN!…”

Intersecting violence: Representations of Somali youth in the Canadian press

“…Topic modeling technique identifies the most recurrent topics found in an unstructured corpus with the use of a statistical model and algorithm; it is part of machine learning that calculates the most frequent words and phrases used and their associations with other terms. Topic modeling has been used in journalism-related studies (Al-Rawi, 2018b; Berendt, 2011; Boumans and Trilling, 2016; Jacobi et al, 2016) such as examining a number of case studies in media coverage (Kang et al, 2013; DiMaggio et al, 2013).…”