False Information Detection

False Information Detection is a computational method designed to systematically identify and verify factually inaccurate or fabricated content within textual data by distinguishing it from other forms of information discrepancy such as misinformation, disinformation, or opinion.

705

Publications

48.3K

Citations

2.2K

Authors

888

Institutions

Publications per year

2017–2026

499

Authors

2.2K

  1. HL
    Huan Liu

    Arizona State University, United States

    9 publications · 3K citations

    9

    h-index

  2. KS
    Kai Shu

    Arizona State University, United States

    9 publications · 2.8K citations

    9

    h-index

  3. SW
    Suhang Wang

    Pennsylvania State University, United States

    5 publications · 2.5K citations

    5

    h-index

  4. LK
    Lisa K. Fazio

    Vanderbilt University, United States

    5 publications · 266 citations

    5

    h-index

  5. PS
    Philip S. Yu

    University of Illinois Chicago, United States

    5 publications · 1.1K citations

    5

    h-index

1–5 of 2.2K

Venues

  1. arXiv (Cornell University)

    Repository · 23 publications · 2K citations

    17

    h-index

  2. SSRN Electronic Journal

    Repository · 8 publications · 263 citations

    8

    h-index

  3. Expert Systems with Applications

    8 publications · 501 citations

    8

    h-index

  4. IEEE Access

    9 publications · 785 citations

    8

    h-index

  5. Procedia Computer Science

    6 publications · 839 citations

    6

    h-index

1–5