Fake news detection: An interdisciplinary research

Xinyi Zhou, Reza Zafarani

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

1 Scopus citations

Abstract

The explosive growth of fake news and its erosion to democracy, journalism and economy has increased the demand for fake news detection. To achieve efficient and explainable fake news detection, an interdisciplinary approach is required, relying on scientific contributions from various disciplines, e.g., social sciences, engineering, among others. Here, we illustrate how such multidisciplinary contributions can help detect fake news by improving feature engineering, or by providing well-justified machine learning models. We demonstrate how news content, news propagation patterns, and users' engagements with news can help detect fake news.

Original languageEnglish (US)
Title of host publicationThe Web Conference 2019 - Companion of the World Wide Web Conference, WWW 2019
PublisherAssociation for Computing Machinery, Inc
Number of pages1
ISBN (Electronic)9781450366755
DOIs
StatePublished - May 13 2019
Event2019 World Wide Web Conference, WWW 2019 - San Francisco, United States
Duration: May 13 2019May 17 2019

Publication series

NameThe Web Conference 2019 - Companion of the World Wide Web Conference, WWW 2019

Conference

Conference2019 World Wide Web Conference, WWW 2019
CountryUnited States
CitySan Francisco
Period5/13/195/17/19

Keywords

  • Disinformation
  • Fake news detection
  • Fake news research
  • False news
  • Misinformation

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Software

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  • Cite this

    Zhou, X., & Zafarani, R. (2019). Fake news detection: An interdisciplinary research. In The Web Conference 2019 - Companion of the World Wide Web Conference, WWW 2019 (The Web Conference 2019 - Companion of the World Wide Web Conference, WWW 2019). Association for Computing Machinery, Inc. https://doi.org/10.1145/3308560.3316476