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Misinformation is not about Bad Facts: An Analysis of the Production and Consumption of Fringe Content (2403.08391v2)

Published 13 Mar 2024 in cs.CL

Abstract: What if misinformation is not an information problem at all? To understand the role of news publishers in potentially unintentionally propagating misinformation, we examine how far-right and fringe online groups share and leverage established legacy news media articles to advance their narratives. Our findings suggest that online fringe ideologies spread through the use of content that is consensus-based and "factually correct". We found that Australian news publishers with both moderate and far-right political leanings contain comparable levels of information completeness and quality; and furthermore, that far-right Twitter users often share from moderate sources. However, a stark difference emerges when we consider two additional factors: 1) the narrow topic selection of articles by far-right users, suggesting that they cherry pick only news articles that engage with their preexisting worldviews and specific topics of concern, and 2) the difference between moderate and far-right publishers when we examine the writing style of their articles. Furthermore, we can identify users prone to sharing misinformation based on their communication style. These findings have important implications for countering online misinformation, as they highlight the powerful role that personal biases towards specific topics and publishers' writing styles have in amplifying fringe ideologies online.

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References (24)
  1. Detecting breaking news rumors of emerging topics in social media. Information Processing & Management, 57(2): 102018.
  2. ‘If your child’s vaccinated, why do you care about mine?’ Rhetoric, responsibility, power and vaccine rejection. Journal of Sociology, 57(2): 268–285.
  3. Riding information crises: the performance of far-right Twitter users in Australia during the 2019–2020 bushfires and the COVID-19 pandemic. Information, Communication & Society, 1–19.
  4. Political ideology predicts perceptions of the threat of COVID-19 (and susceptibility to fake news about it). Social Psychological and Personality Science, 11(8): 1119–1128.
  5. What do we know when we LIWC a person? Text analysis as an assessment tool for traits, personal concerns and life stories. The Sage handbook of personality and individual differences, 341–360.
  6. Cohen, J. 2013. Statistical power analysis for the behavioral sciences. Routledge.
  7. A text as unique as a fingerprint: Text analysis and authorship recognition in a Virtual Learning Environment of the Unified Health System in Brazil. Expert Systems with Applications, 203: 117280.
  8. Rethinking fake news: Disinformation and ideology during the time of COVID-19 global pandemic. IIM Kozhikode Society & Management Review, 11(1): 146–159.
  9. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805.
  10. This just in: Fake news packs a lot in title, uses simpler, repetitive content in text body, more similar to satire than real news. In ICWSM, volume 11, 759–766.
  11. Stylometric Detection of AI-Generated Text in Twitter Timelines. arXiv:2303.03697.
  12. Kydd, A. H. 2021. Decline, radicalization and the attack on the US Capitol. Violence: An International Journal, 2(1): 3–23.
  13. Whose Advantage? Measuring Attention Dynamics across YouTube and Twitter on Controversial Topics. In ICWSM, volume 16, 573–583.
  14. Reuters Institute digital news report 2021. Reuters Institute for the Study of Journalism.
  15. Styles with Benefits. The StyloMetrix Vectors for Stylistic and Semantic Text Classification of Small-Scale Datasets and Different Sample Length.
  16. Digital news report: Australia 2021. News and Media Research Centre (UC).
  17. Fake news detection based on news content and social contexts: a transformer-based approach. International Journal of Data Science and Analytics, 13(4): 335–362.
  18. Fake news detection related to the covid-19 in slovak language using deep learning methods. Acta Polytechnica Hungarica, 19(2): 43–57.
  19. News use across social media platforms in 2020.
  20. Stankov, L. 2021. From social conservatism and authoritarian populism to militant right-wing extremism. Personality and individual differences, 175: 110733.
  21. The psychological meaning of words: LIWC and computerized text analysis methods. Journal of language and social psychology, 29(1): 24–54.
  22. The grievance dictionary: Understanding threatening language use. Behavior research methods, 1–15.
  23. Same Author or Just Same Topic? Towards Content-Independent Style Representations. In Gella, S.; He, H.; Majumder, B. P.; Can, B.; Giunchiglia, E.; Cahyawijaya, S.; Min, S.; Mozes, M.; Li, X. L.; Augenstein, I.; Rogers, A.; Cho, K.; Grefenstette, E.; Rimell, L.; and Dyer, C., eds., Proceedings of the 7th Workshop on Representation Learning for NLP, 249–268. Dublin, Ireland: Association for Computational Linguistics.
  24. Evaluation of fake news detection with knowledge-enhanced language models. In ICWSM, volume 16, 1425–1429.
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Authors (4)
  1. Emily Booth (3 papers)
  2. Hany Farid (20 papers)
  3. Marian-Andrei Rizoiu (62 papers)
  4. Jooyoung Lee (48 papers)
Citations (1)
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