Fake News: Fundamental theories, detection strategies and challenges

Xinyi Zhou, Reza Zafarani, Kai Shu, Huan Liu

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

16 Scopus citations

Abstract

The explosive growth of fake news and its erosion to democracy, justice, and public trust increased the demand for fake news detection. As an interdisciplinary topic, the study of fake news encourages a concerted effort of experts in computer and information science, political science, journalism, social science, psychology, and economics. A comprehensive framework to systematically understand and detect fake news is necessary to attract and unite researchers in related areas to conduct research on fake news. This tutorial aims to clearly present (1) fake news research, its challenges, and research directions; (2) a comparison between fake news and other related concepts (e.g., rumours); (3) the fundamental theories developed across various disciplines that facilitate interdisciplinary research; (4) various detection strategies unified under a comprehensive framework for fake news detection; and (5) the state-of-the-art datasets, patterns, and models. We present fake news detection from various perspectives, which involve news content and information in social networks, and broadly adopt techniques in data mining, machine learning, natural language processing, information retrieval and social search. Facing the upcoming 2020 U.S. presidential election, challenges for automatic, effective and efficient fake news detection are also clarified in this tutorial.

Original languageEnglish (US)
Title of host publicationWSDM 2019 - Proceedings of the 12th ACM International Conference on Web Search and Data Mining
PublisherAssociation for Computing Machinery, Inc
Pages836-837
Number of pages2
ISBN (Electronic)9781450359405
DOIs
StatePublished - Jan 30 2019
Event12th ACM International Conference on Web Search and Data Mining, WSDM 2019 - Melbourne, Australia
Duration: Feb 11 2019Feb 15 2019

Publication series

NameWSDM 2019 - Proceedings of the 12th ACM International Conference on Web Search and Data Mining

Conference

Conference12th ACM International Conference on Web Search and Data Mining, WSDM 2019
CountryAustralia
CityMelbourne
Period2/11/192/15/19

Keywords

  • Fake news
  • Fake news detection
  • News verification

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Software
  • Computer Science Applications

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  • Cite this

    Zhou, X., Zafarani, R., Shu, K., & Liu, H. (2019). Fake News: Fundamental theories, detection strategies and challenges. In WSDM 2019 - Proceedings of the 12th ACM International Conference on Web Search and Data Mining (pp. 836-837). (WSDM 2019 - Proceedings of the 12th ACM International Conference on Web Search and Data Mining). Association for Computing Machinery, Inc. https://doi.org/10.1145/3289600.3291382