FakeSwarm: Improving Fake News Detection with Swarming Characteristics
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Abstract (arXiv)
The proliferation of fake news poses a serious threat to society, as it can misinform and manipulate the public, erode trust in institutions, and undermine democratic processes. To address this issue, we present FakeSwarm, a fake news identification system that leverages the swarming characteristics of fake news. To extract the swarm behavior, we propose a novel concept of fake news swarming characteristics and design three types of swarm features, including principal component analysis, metric representation, and position encoding. We evaluate our system on a public dataset and demonstrate the effectiveness of incorporating swarm features in fake news identification, achieving an f1-score and accuracy of over 97% by combining all three types of swarm features. Furthermore, we design an online learning pipeline based on the hypothesis of the temporal distribution pattern of fake news emergence, validated on a topic with early emerging fake news and a shortage of text samples, showing that swarm features can significantly improve recall rates in such cases. Our work provides a new perspective and approach to fake news detection and highlights the importance of considering swarming characteristics in detecting fake news.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 20 of its 41 references that are in the map. First posted on arXiv 30 May 2023.
Builds on
20 of 41 references.
References · 20
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8 more
- 2021Heterogeneity-aware Twitter Bot Detection with Relational Graph Transformerscited by 41
- 2022Seeing is Living? Rethinking the Security of Facial Liveness Verification in the Deepfake Eracited by 27
- 2023BotTriNet: A Unified and Efficient Embedding for Social Bots Detection via Metric Learningcited by 13
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Citing papers per year: 2023: 1.
1 paper.