Misinformation Detection: A Survey of AI Techniques and Research Opportunities

Gabrielle Taylor, Wenting Jiang, Xiao Qin and Ashish Gupta

Foundations and Trends(R) in Information Systems, 2024, vol. 8, issue 2, 66-147

Abstract: This survey highlights the evolution of techniques within misinformation detection. Misinformation has become increasingly prevalent on the Internet by the day and progressively more threatening. Individuals who are inaccurately informed tend to make misinformed decisions which have led to voting scandals, traffic accidents, and even health concerns. We are motivated to address a research gap by analyzing misinformation detection’s overall progress and exposing the weaknesses that provide research opportunities. Our findings will further advance the work of misinformation detection and bring light to unique ways to tackle the issue. Notably, we discuss the significance of misinformation detection systems and present the problems resulting from misinformation, the techniques for detection, and open issues within this research. Misinformation is becoming an issue that requires more attention and improved systems. We believe that our systematic review and synthesis of state-of-art research will cultivate a path for these developments.

Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
http://dx.doi.org/10.1561/2900000037 (application/xml)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:now:fntisy:2900000037

Access Statistics for this article

More articles in Foundations and Trends(R) in Information Systems from now publishers
Bibliographic data for series maintained by Lucy Wiseman ().