Misinformation, Disinformation, and Online Propaganda
Abstract
The research literature on misinformation, disinformation, and propaganda is vast and sprawling. This chapter discusses descriptive research on the supply and availability of misinformation, patterns of exposure and consumption, and what is known about mechanisms behind its spread through networks. It provides a brief overview of the literature on misinformation in political science and psychology, which provides a basis for understanding the phenomena discussed here. It then examines what we know about the effects of misinformation and how it is studied. It concludes with a discussion of gaps in our knowledge and future directions in research in this area.
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The winning team in the AAAI 2021 COVID-19 Fake News Detection challenge achieved a weighted F1-score of 0.987 in classifying fake news from social media post data.
“For example, in the AAAI 2021 COVID-19 Fake News Detection challenge [83], the winning team achieved a weighted F1-score of 0.987 in classifying fake news from social media post data [37].”
From Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions (2023) · cites this paper as [37]
CT-BERT achieved a weighted F1-score of 0.987 on the final blinded test set.
“The model achieved a weighted F1-score of 0.987 on the fnal blinded test set.”
From Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions (2023) · cites this paper as [37]
CT-BERT was a transformer-based model pretrained on COVID-19-related Twitter documents collected from January to April 2020.
“Briefy, CT-BERT was a transformerbased model pretrained on COVID-19-related Twitter documents collected from January to April 2020 [37].”
From Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions (2023) · cites this paper as [37]
Recent advances in technology for hyper-realistic manipulations to video pose a threat to traditional standards of evidence and trust in media.
“Recent advances in technology for algorithmically applying hyper-realistic manipulations to video are simultaneously enabling new forms of interpersonal communication and posing a threat to traditional standards of evidence and trust in media1–8.”
From Human detection of political speech deepfakes across transcripts, audio, and video (2024) · cites this paper as [7]
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- Authors
- Guess, A. M., & Lyons, B. A.
- Venue
- Social Media and Democracy
- Publisher
- Cambridge University Press
- Published
- 2020-08-31
- Pages
- 10-33
- OpenAlex
- W3081379046
- Cited by
- 284
- References
- 83
- Citations by year
- 2026: 48 · 2025: 68 · 2024: 62 · 2023: 48 · 2022: 43 · 2021: 15
- FWCI
- 181.90
- Type
- book chapter
- Language
- en
- Open access
- closed
- Locations
- Cambridge University Press eBooks (publishedVersion)
- Topics
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Social Sciences › Communication › Social Media and Politics
- Social Sciences › Sociology and Political Science › Media Influence and Politics
- Keywords
- misinformation, disinformation, online propaganda, political science perspective
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