Domain Adaptive Fake News Detection via Reinforcement Learning

Ahmadreza Mosallanezhad, Mansooreh Karami, Kai Shu, Michelle V. Mancenido, Huan Liu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

With social media being a major force in information consumption, accelerated propagation of fake news has presented new challenges for platforms to distinguish between legitimate and fake news. Effective fake news detection is a non-trivial task due to the diverse nature of news domains and expensive annotation costs. In this work, we address the limitations of existing automated fake news detection models by incorporating auxiliary information (e.g., user comments and user-news interactions) into a novel reinforcement learning-based model called REinforced Adaptive Learning Fake News Detection (REAL-FND). REAL-FND exploits cross-domain and within-domain knowledge that makes it robust in a target domain, despite being trained in a different source domain. Extensive experiments on real-world datasets illustrate the effectiveness of the proposed model, especially when limited labeled data is available in the target domain.

Original languageEnglish (US)
Title of host publicationWWW 2022 - Proceedings of the ACM Web Conference 2022
PublisherAssociation for Computing Machinery, Inc
Pages3632-3640
Number of pages9
ISBN (Electronic)9781450390965
DOIs
StatePublished - Apr 25 2022
Externally publishedYes
Event31st ACM World Wide Web Conference, WWW 2022 - Virtual, Online, France
Duration: Apr 25 2022Apr 29 2022

Publication series

NameWWW 2022 - Proceedings of the ACM Web Conference 2022

Conference

Conference31st ACM World Wide Web Conference, WWW 2022
Country/TerritoryFrance
CityVirtual, Online
Period4/25/224/29/22

Keywords

  • disinformation
  • domain adaptation
  • neural networks
  • reinforcement learning

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Software

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