arbiter
JournalsPricingAccount

Misinformation Detection: A Survey of AI Techniques and Research Opportunities

Gabrielle Taylor et al.

Foundations and Trends® in Information Systems2024https://doi.org/10.1561/2900000037article
AJG 1
Weight
0.43

What the paper says

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.

1 citation

Open paper page →

Cite this paper

https://doi.org/https://doi.org/10.1561/2900000037

Or copy a formatted citation

@article{gabrielle2024,
  title        = {{Misinformation Detection: A Survey of AI Techniques and Research Opportunities}},
  author       = {Gabrielle Taylor et al.},
  journal      = {Foundations and Trends® in Information Systems},
  year         = {2024},
  doi          = {https://doi.org/https://doi.org/10.1561/2900000037},
}

Paste directly into BibTeX, Zotero, or your reference manager.

Flag this paper

Misinformation Detection: A Survey of AI Techniques and Research Opportunities

Flags are reviewed by the Arbiter methodology team within 5 business days.

Evidence weight

0.43

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.32 × 0.4 = 0.13
M · momentum0.53 × 0.15 = 0.08
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.