Detecting and Mitigating the Dissemination of Fake News: Challenges and Future Research Opportunities

【Author】 Shahid, Wajiha; Jamshidi, Bahman; Hakak, Saqib; Isah, Haruna; Khan, Wazir Zada; Khan, Muhammad Khurram; Choo, Kim-Kwang Raymond

【Source】IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS

【影响因子】4.747

【Abstract】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.

【Keywords】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

【发表时间】

【收录时间】2022-06-22

【文献类型】实证性文章

【主题类别】

区块链应用-实体经济-文化领域