Fake News Detection in Social Networks via Crowd Signals
Open accessAbstract
Our work considers leveraging crowd signals for detecting fake news and is motivated by tools recently introduced by Facebook that enable users to flag fake news. By aggregating users' flags, our goal is to select a small subset of news every day, send them to an expert (e.g., via a third-party fact-checking organization), and stop the spread of news identified as fake by an expert. The main objective of our work is to minimize the spread of misinformation by stopping the propagation of fake news in the network. It is especially challenging to achieve this objective as it requires detecting fake news with high-confidence as quickly as possible. We show that in order to leverage users' flags efficiently, it is crucial to learn about users' flagging accuracy. We develop a novel algorithm, DETECTIVE, that performs Bayesian inference for detecting fake news and jointly learns about users' flagging accuracy over time. Our algorithm employs posterior sampling to actively trade off exploitation (selecting news that maximize the objective value at a given epoch) and exploration (selecting news that maximize the value of information towards learning about users' flagging accuracy). We demonstrate the effectiveness of our approach via extensive experiments and show the power of leveraging community signals for fake news detection.
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Crowdsourcing methods use the combined input and signals from many individuals to pool collective intelligence for detecting false content and limiting misinformation.
“These approaches rely on the collective contributions and crowd signals (Tschiatschek et al. 2018) of a group of people for the aggregation of crowd intelligence to detect fake news (Tchakounté et al. 2020) and to reduce the spread of misinformation on social media (Pennycook and Rand 2019; Micallef et al. 2020).”
From Fake news, disinformation and misinformation in social media: a review (2023) · cites this paper as Tschiatschek et al. 2018
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Generating Literal and Implied Subquestions to Fact-check Complex Claims
Chen, J., Sriram, A., Choi, E., & Durrett, G. (2022) · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2022.emnlp-main.229 · Cited by 34 · see citing works
Mitigating Misinformation in Online Social Network with Top-k Debunkers and Evolving User Opinions
Saxena, A., Hsu, W., Lee, M. L., Chieu, H. L., Ng, L., & Teow, L.-N. (2020) · The Web Conference · doi:10.1145/3366424.3383297 · Cited by 38 · see citing works
Connecting the Dots Between Fact Verification and Fake News Detection
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Hy-DeFake: Hypergraph neural networks for detecting fake news in online social networks
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Early Detection of Fake News "Before It Flies High"
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A Unified Perspective for Disinformation Detection and Truth Discovery in Social Sensing: A Survey
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The Future of Misinformation Detection: New Perspectives and TrendsPreprint
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Detecting Fake News on Social Media
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Preventing rumor spread with deep learning
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A Comprehensive Survey of Fake Text Detection on Misinformation and LM-Generated Texts
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Homogeneity-Based Transmissive Process to Model True and False News in Social Networks
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Almaliki, M. (2019) · International Conference on Information System and Data Mining (ICISDM) · doi:10.1145/3325917.3325938 · Cited by 25 · see citing works
Fake News Filtering: Semantic Approaches
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The Spread of Disinformation on the Web: An Examination of Memes on Social Networking
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Modeling and Measuring Expressed (Dis)belief in (Mis)information
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Propagation of Fake News on Social Media: Challenges and Opportunities
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A Reliable Weighting Scheme for the Aggregation of Crowd Intelligence to Detect Fake News
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Citizen Perceptions of Fake News in Spain: Socioeconomic, Demographic, and Ideological Differences
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False News On Social Media: A Data-Driven SurveyPreprint
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Beyond Text: Multimodal Credibility Assessment Approaches for Online User-Generated Content
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Dynamic Probabilistic Graphical Model for Progressive Fake News Detection on Social Media Platform
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Enhancing Public Resistance to “Fake News”
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A Machine Learning Technique for Detection of Social Media Fake News
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Message Distortion in Information Cascades
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Distortions of political bias in crowdsourced misinformation flagging
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Multi-criteria assessment of user trust in Social Reviewing Systems with subjective logic fusion
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Using Fuzzy Clustering with Deep Learning Models for Detection of COVID-19 Disinformation
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- Authors
- Tschiatschek, S., Singla, A., Gomez Rodriguez, M., Merchant, A., & Krause, A.
- Affiliations
- Microsoft Research (United Kingdom) (GB)
- Max Planck Institute for Software Systems (DE)
- MPI-SWS, Kaiserslautern, Germany
- International Institute of Information Technology, Hyderabad (IN)
- ETH Zurich (CH)
- Venue
- Companion of the The Web Conference 2018 on The Web Conference 2018 - WWW '18
- Publisher
- ACM Press
- Published
- 2018
- Pages
- 517-524
- OpenAlex
- W2793255598
- Cited by
- 229
- References
- 37
- Citations by year
- 2026: 2 · 2025: 13 · 2024: 19 · 2023: 27 · 2022: 38 · 2021: 34 · 2020: 48 · 2019: 39 · 2018: 9
- FWCI
- 99.04
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- International World Wide Web Conference (WWW) (publishedVersion, cc-by)
- Funders
- National Science Foundation
- Microsoft Research
- Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
- FP7 Ideas: European Research Council (307036)
- Topics
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Physical Sciences › Computer Science › Information Systems › Spam and Phishing Detection
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Keywords
- fake news detection, misinformation spread, fact-checking, Bayesian inference, posterior sampling, exploration-exploitation balance, social networks
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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