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Evolving to the Future: Unseen Event Adaptive Fake News Detection on Social Media

Jiajun Zhang, Zhixun Li, Qiang Liu, Shu Wu, Liang Wang

TL;DR

This work introduces Future AD aptive Event-based Fake news Detection (FADE) framework, which trains a target predictor through an adaptive augmentation strategy and graph contrastive learning to obtain higher-quality features and make more accurate overall predictions.

Abstract

With the rapid development of social media, the wide dissemination of fake news on social media is increasingly threatening both individuals and society. One of the unique challenges for fake news detection on social media is how to detect fake news on future events. Recently, numerous fake news detection models that utilize textual information and the propagation structure of posts have been proposed. Unfortunately, most of the existing approaches can hardly handle this challenge since they rely heavily on event-specific features for prediction and cannot generalize to unseen events. To address this, we introduce \textbf{F}uture \textbf{AD}aptive \textbf{E}vent-based Fake news Detection (FADE) framework. Specifically, we train a target predictor through an adaptive augmentation strategy and graph contrastive learning to obtain higher-quality features and make more accurate overall predictions. Simultaneously, we independently train an event-only predictor to obtain biased predictions. We further mitigate event bias by subtracting the event-only predictor's output from the target predictor's output to obtain the final prediction. Encouraging results from experiments designed to emulate real-world social media conditions validate the effectiveness of our method in comparison to existing state-of-the-art approaches.

Evolving to the Future: Unseen Event Adaptive Fake News Detection on Social Media

TL;DR

This work introduces Future AD aptive Event-based Fake news Detection (FADE) framework, which trains a target predictor through an adaptive augmentation strategy and graph contrastive learning to obtain higher-quality features and make more accurate overall predictions.

Abstract

With the rapid development of social media, the wide dissemination of fake news on social media is increasingly threatening both individuals and society. One of the unique challenges for fake news detection on social media is how to detect fake news on future events. Recently, numerous fake news detection models that utilize textual information and the propagation structure of posts have been proposed. Unfortunately, most of the existing approaches can hardly handle this challenge since they rely heavily on event-specific features for prediction and cannot generalize to unseen events. To address this, we introduce \textbf{F}uture \textbf{AD}aptive \textbf{E}vent-based Fake news Detection (FADE) framework. Specifically, we train a target predictor through an adaptive augmentation strategy and graph contrastive learning to obtain higher-quality features and make more accurate overall predictions. Simultaneously, we independently train an event-only predictor to obtain biased predictions. We further mitigate event bias by subtracting the event-only predictor's output from the target predictor's output to obtain the final prediction. Encouraging results from experiments designed to emulate real-world social media conditions validate the effectiveness of our method in comparison to existing state-of-the-art approaches.
Paper Structure (15 sections, 10 equations, 2 figures, 3 tables)

This paper contains 15 sections, 10 equations, 2 figures, 3 tables.

Table of Contents

  1. Introduction
  2. PROPOSED APPROACH
  3. Problem Definition
  4. Model Overview
  5. GCN-based Encoder
  6. Adaptive Graph Augmentation
  7. Target Predictor
  8. Event-Only Predictor
  9. Debias in inference stage
  10. Experiments
  11. Experiment Settings
  12. Compared Methods
  13. Result and Discussion
  14. Conclusion
  15. Acknowledgments

Figures (2)

  • Figure 1: Comparison of mean accuracy over 10 runs for each approach in event-mixed and event-separated settings. Note PSA-S is designed for event-separated scenarios.
  • Figure 2: Overview of our FADE framework. In the training stage, the target predictor and event-only predictor are trained independently. In the inference stage, we use both predictors' outputs to perform causal debiasing.