FakeSwarm: Improving Fake News Detection with Swarming Characteristics
arXiv:2305.19194 · doi:10.5121/csit.2023.130815
Abstract
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.
9th International Conference on Data Mining and Applications (DMA 2023). Keywords: Fake News Detection, Metric Learning, Clustering, Dimensionality Reduction
References in corpus (9)
- Fake News Detection on Social Media: A Data Mining Perspective
- Fake News Detection on Social Media using Geometric Deep Learning
- Towards Robust LiDAR-based Perception in Autonomous Driving: General Black-box Adversarial Sensor Attack and Countermeasures
- BotShape: A Novel Social Bots Detection Approach via Behavioral Patterns
- Online User Profiling to Detect Social Bots on Twitter
- Seeing is Living? Rethinking the Security of Facial Liveness Verification in the Deepfake Era
- Heterogeneity-aware Twitter Bot Detection with Relational Graph Transformers
- BotTriNet: A Unified and Efficient Embedding for Social Bots Detection via Metric Learning
- C2PI: An Efficient Crypto-Clear Two-Party Neural Network Private Inference