期刊
IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS
卷 -, 期 -, 页码 -出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCSS.2022.3177359
关键词
Fake news; Feature extraction; Social networking (online); Artificial intelligence; Computer science; Taxonomy; Support vector machines; Artificial intelligence (AI) explainability; blockchain-based detection; deceptive content; deep fakes; fake news; misinformation; news propaganda; social bots; social media
资金
- New Brunswick Innovation Fund (NBIF) [RAI 2021-057]
Fake news poses a significant threat to democracy, especially in our current socially and digitally connected society. Despite research from various disciplines on detecting and mitigating fake news, it remains challenging to prevent its dissemination effectively. Designing artificial intelligence systems that can provide detailed explanations of fake news detection is crucial in combating this issue.
Fake news is a major threat to democracy (e.g., influencing public opinion), and its impact cannot be understated particularly in our current socially and digitally connected society. Researchers from different disciplines (e.g., computer science, political science, information science, and linguistics) have also studied the dissemination, detection, and mitigation of fake news; however, it remains challenging to detect and prevent the dissemination of fake news in practice. In addition, we emphasize the importance of designing artificial intelligence (AI)-powered systems that are capable of providing detailed, yet user-friendly, explanations of the classification / detection of fake news. Hence, in this article, we systematically survey existing state-of-the-art approaches designed to detect and mitigate the dissemination of fake news, and based on the analysis, we discuss several key challenges and present a potential future research agenda, especially incorporating AI explainable fake news credibility system.
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