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A Survey of Fake News:Fundamental Theories,Detection Methods,and Opportunities - 2020

Research Area:  Machine Learning

Abstract:

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.

Author(s) Name:  Xinyi Zhou , Reza Zafarani

Journal name:  ACM Computing Surveys

Conferrence name:  

Publisher name:  ACM

DOI:  10.1145/3395046

Volume Information:  Volume 53,Issue 5,September 2021,Article No.: 109,pp 1–40