How Algorithms Create and Prevent Fake News: Exploring the Impacts of Social Media, Deepfakes, GPT-3, and More
Abstract
See how algorithms create and prevent fake news in this book that tells the stories of this algorithmic battle for the truth.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Deepfakes pose significant problems across various domains, such as misinformation, political manipulation, propaganda, reputational damage, and fraud.
“Deepfakes can be used for entertainment, education, and research; however, they pose a range of significant problems across various domains, such as misinformation, political manipulation, propaganda, reputational damage, and fraud.”
From Deepfake Attacks: Generation, Detection, Datasets, Challenges, and Research Directions (2023) · cites this paper as [2]
Cited by
Sorted by citations
Deepfakes: Deceptions, mitigations, and opportunities
Mustak, M., Salminen, J., Mäntymäki, M., Rahman, A., & Dwivedi, Y. K. (2023) · Journal of Business Research · doi:10.1016/j.jbusres.2022.113368 · Cited by 209 · see citing works
Deepfake Attacks: Generation, Detection, Datasets, Challenges, and Research Directions
Naitali, A., Ridouani, M., Salahdine, F., & Kaabouch, N. (2023) · Computers · doi:10.3390/computers12100216 · Cited by 117 · see citing works
Evaluating Twitter’s algorithmic amplification of low-credibility content: an observational study
Corsi, G. (2024) · EPJ Data Science · doi:10.1140/epjds/s13688-024-00456-3 · Cited by 45 · see citing works
- Authors
- Giansiracusa, N.
- Corresponding author
- Giansiracusa, Noah
- ORCID
- Giansiracusa, Noah
- Venue
- Apress eBooks
- Publisher
- Apress
- Published
- 2021
- OpenAlex
- W4252380943
- Cited by
- 42
- References
- 0
- Citations by year
- 2026: 4 · 2025: 7 · 2024: 14 · 2023: 9 · 2022: 8
- FWCI
- 11.36
- Type
- book
- Language
- en
- Open access
- closed
- Locations
- Apress eBooks (publishedVersion)
- Topics
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Information Systems › Spam and Phishing Detection
- Keywords
- fake news, misinformation, algorithms, algorithmic bias, content moderation, truth, social media
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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