Fake News Early Detection: A Theory-driven Model

Xinyi Zhou, Atishay Jain, Vir V. Phoha, Reza Zafarani

Research output: Contribution to journalArticlepeer-review

153 Scopus citations


Massive dissemination of fake news and its potential to erode democracy has increased the demand for accurate fake news detection. Recent advancements in this area have proposed novel techniques that aim to detect fake news by exploring how it propagates on social networks. Nevertheless, to detect fake news at an early stage, i.e., when it is published on a news outlet but not yet spread on social media, one cannot rely on news propagation information as it does not exist. Hence, there is a strong need to develop approaches that can detect fake news by focusing on news content. In this article, a theory-driven model is proposed for fake news detection. The method investigates news content at various levels: lexicon-level, syntax-level, semantic-level, and discourse-level. We represent news at each level, relying on well-established theories in social and forensic psychology. Fake news detection is then conducted within a supervised machine learning framework. As an interdisciplinary research, our work explores potential fake news patterns, enhances the interpretability in fake news feature engineering, and studies the relationships among fake news, deception/disinformation, and clickbaits. Experiments conducted on two real-world datasets indicate the proposed method can outperform the state-of-the-art and enable fake news early detection when there is limited content information.

Original languageEnglish (US)
Article number12
JournalDigital Threats: Research and Practice
Issue number2
StatePublished - Jul 2020


  • Fake news
  • click-bait
  • disinformation
  • fake news detection
  • feature engineering
  • interdisciplinary research
  • news verification

ASJC Scopus subject areas

  • Software
  • Information Systems
  • Hardware and Architecture
  • Computer Networks and Communications
  • Computer Science Applications
  • Safety Research


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