A Survey of Fake News: Fundamental Theories, Detection Methods, and Opportunities

Xinyi Zhou, Reza Zafarani

Research output: Contribution to journalArticlepeer-review

580 Scopus citations


The explosive growth in fake news and its erosion to democracy, justice, and public trust has increased the demand for fake news detection and intervention. This survey reviews and evaluates methods that can detect fake news from four perspectives: the false knowledge it carries, its writing style, its propagation patterns, and the credibility of its source. The survey also highlights some potential research tasks based on the review. In particular, we identify and detail related fundamental theories across various disciplines to encourage interdisciplinary research on fake news. It is our hope that this survey can facilitate collaborative efforts among experts in computer and information sciences, social sciences, political science, and journalism to research fake news, where such efforts can lead to fake news detection that is not only efficient but, more importantly, explainable.

Original languageEnglish (US)
Article number3395046
JournalACM Computing Surveys
Issue number5
StatePublished - Sep 28 2020


  • Fake news
  • deception detection
  • disinformation
  • fact-checking
  • information credibility
  • knowledge graph
  • misinformation
  • news verification
  • social media

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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