FACTOID: A New Dataset for Identifying Misinformation Spreaders and Political Bias
Not on the map. Why?
Abstract (arXiv)
Proactively identifying misinformation spreaders is an important step towards mitigating the impact of fake news on our society. In this paper, we introduce a new contemporary Reddit dataset for fake news spreader analysis, called FACTOID, monitoring political discussions on Reddit since the beginning of 2020. The dataset contains over 4K users with 3.4M Reddit posts, and includes, beyond the users' binary labels, also their fine-grained credibility level (very low to very high) and their political bias strength (extreme right to extreme left). As far as we are aware, this is the first fake news spreader dataset that simultaneously captures both the long-term context of users' historical posts and the interactions between them. To create the first benchmark on our data, we provide methods for identifying misinformation spreaders by utilizing the social connections between the users along with their psycho-linguistic features. We show that the users' social interactions can, on their own, indicate misinformation spreading, while the psycho-linguistic features are mostly informative in non-neural classification settings. In a qualitative analysis, we observe that detecting affective mental processes correlates negatively with right-biased users, and that the openness to experience factor is lower for those who spread fake news.
Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). First posted on arXiv 11 May 2022.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2022: 1; 2023: 2; 2024: 4; 2025: 2; 2026: 1.
10 papers.
- 2025A Survey on Mechanistic Interpretability for Multi-Modal Foundation Modelscited by 34
- 2022Unifying Data Perspectivism and Personalization: An Application to Social Normscited by 22
- 2023Exploring news intent and its application: A theory-driven approachcited by 7
- 2024Towards Fair Graph Anomaly Detection: Problem, Benchmark Datasets, and Evaluationcited by 6
- 2024MisinfoEval: Generative AI in the Era of "Alternative Facts"cited by 6
- 2023An Interactive Framework for Profiling News Media Sourcescited by 4
- 2025Toxicity in Online Platforms and AI Systems: A Survey of Needs, Challenges, Mitigations, and Future Directionscited by 3
- 2024News Source Credibility Assessment: A Reddit Case Studycited by 1
- 2026SpatialJB: How Text Distribution Art Becomes the "Jailbreak Key" for LLM Guardrailscited by 1
- 2024Clustering Document Parts: Detecting and Characterizing Influence Campaigns from Documentscited by 0
- 2026SpatialJB: How Text Distribution Art Becomes the "Jailbreak Key" for LLM Guardrails14 Jan 2026
- 2025Toxicity in Online Platforms and AI Systems: A Survey of Needs, Challenges, Mitigations, and Future Directions29 Sep 2025
- 2025A Survey on Mechanistic Interpretability for Multi-Modal Foundation Models22 Feb 2025
- 2024MisinfoEval: Generative AI in the Era of "Alternative Facts"13 Oct 2024
- 2024Clustering Document Parts: Detecting and Characterizing Influence Campaigns from Documents27 Feb 2024
- 2024Towards Fair Graph Anomaly Detection: Problem, Benchmark Datasets, and Evaluation25 Feb 2024
- 2024News Source Credibility Assessment: A Reddit Case Study7 Feb 2024
- 2023Exploring news intent and its application: A theory-driven approach27 Dec 2023
- 2023An Interactive Framework for Profiling News Media Sources14 Sep 2023
- 2022Unifying Data Perspectivism and Personalization: An Application to Social Norms26 Oct 2022