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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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“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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Liu, Z., Shan, J., Delaloye, M., Piguet, J.-G., & Balet, N. G. (2020) · Journalism and Media · doi:10.3390/journalmedia1010010 · Cited by 19
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Esposito, C., Galli, A., Moscato, V., & Sperlí, G. (2021) · Information Fusion · doi:10.1016/j.inffus.2021.07.012 · Cited by 18
Using Fuzzy Clustering with Deep Learning Models for Detection of COVID-19 Disinformation
Chen, M., & Lai, Y.-W. (2022) · ACM Transactions on Asian and Low-Resource Language Information Processing · doi:10.1145/3548458 · Cited by 19
Metadata
- 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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