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- Xinyi Zhou
- Syracuse University, Syracuse, NY
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- Reza Zafarani
- Syracuse University, Syracuse, NY
書誌事項
- タイトル別名
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- Fundamental Theories, Detection Methods, and Opportunities
説明
<jats:p> 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 <jats:italic>knowledge</jats:italic> it carries, its writing <jats:italic>style</jats:italic> , its <jats:italic>propagation</jats:italic> patterns, and the credibility of its <jats:italic>source</jats:italic> . 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. </jats:p>
収録刊行物
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- ACM Computing Surveys
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ACM Computing Surveys 53 (5), 1-40, 2020-09-28
Association for Computing Machinery (ACM)
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詳細情報 詳細情報について
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- CRID
- 1360294645461976320
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- DOI
- 10.1145/3395046
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- ISSN
- 15577341
- 03600300
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- データソース種別
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- Crossref