Enhancing Fake News Detection in Social Media via Label Propagation on Cross-modal Tweet Graph

TL;DR

This paper introduces a novel cross-modal graph-based approach using CLIP and label propagation to improve fake news detection in social media, addressing graph sparsity and enhancing generalization to unseen events.

Contribution

It proposes a densified cross-modal tweet graph with label propagation and a domain generalization loss, advancing fake news detection accuracy and robustness.

Findings

Outperforms state-of-the-art methods on Twitter, PHEME, and Weibo datasets.

Effectively captures tweet interactions via cross-modal similarities.

Enhances generalization to unseen events with domain loss.

Abstract

Fake news detection in social media has become increasingly important due to the rapid proliferation of personal media channels and the consequential dissemination of misleading information. Existing methods, which primarily rely on multimodal features and graph-based techniques, have shown promising performance in detecting fake news. However, they still face a limitation, i.e., sparsity in graph connections, which hinders capturing possible interactions among tweets. This challenge has motivated us to explore a novel method that densifies the graph's connectivity to capture denser interaction better. Our method constructs a cross-modal tweet graph using CLIP, which encodes images and text into a unified space, allowing us to extract potential connections based on similarities in text and images. We then design a Feature Contextualization Network with Label Propagation (FCN-LP) to model the interaction among tweets as well as positive or negative correlations between predicted labels of connected tweets. The propagated labels from the graph are weighted and aggregated for the final detection. To enhance the model's generalization ability to unseen events, we introduce a domain generalization loss that ensures consistent features between tweets on seen and unseen events. We use three publicly available fake news datasets, Twitter, PHEME, and Weibo, for evaluation. Our method consistently improves the performance over the state-of-the-art methods on all benchmark datasets and effectively demonstrates its aptitude for generalizing fake news detection in social media.