Improving Rumor Detection by Promoting Information Campaigns with Transformer-based Generative Adversarial Learning

Jing Ma, Jun Li, Wei Gao*, Yang Yang, Kam Fai Wong

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Rumors can cause devastating consequences to individuals and our society. Analysis shows that the widespread of rumors typically results from deliberate promotion of information aiming to shape the collective public opinions on the concerned event. In this paper, we combat such chaotic phenomenon with a countermeasure by mirroring against how such chaos is created to make rumor detection more robust and effective. Our idea is inspired by adversarial learning method originated from Generative Adversarial Networks (GAN). We propose a GAN-style approach, where a generator is designed to produce uncertain or conflicting voices, further polarizing the original conversational threads to boost the discriminator. We reveal that feature learning effectiveness is highly relevant to the quality of generated parody. Given the strong natural language generation performance of transformer, we propose a transformer-based method to improve the generated posts, which appear to be closely responsive to the source post and retain the authentic propagation structure. Different from traditional data-driven rumor detection approaches, our method can capture low-frequency but more salient non-trivial discriminant patterns. Extensive experiments on THREE benchmarks demonstrate that our rumor detection method achieves much better results than state-of-the-art methods, and the transformer-based model further improve the performance of our GAN-style approach.

Original languageEnglish
Number of pages14
JournalIEEE Transactions on Knowledge and Data Engineering
DOIs
Publication statusE-pub ahead of print - 16 Sep 2021

Scopus Subject Areas

  • Information Systems
  • Computer Science Applications
  • Computational Theory and Mathematics

User-Defined Keywords

  • Blogs
  • Feature extraction
  • Generative Adversarial Networks
  • Generators
  • Information Campaigns
  • Recurrent neural networks
  • Rumor Detection
  • Self-attention
  • Social networking (online)
  • Training
  • Transformer
  • Transformers

Fingerprint

Dive into the research topics of 'Improving Rumor Detection by Promoting Information Campaigns with Transformer-based Generative Adversarial Learning'. Together they form a unique fingerprint.

Cite this