False Claims Detection

False Claims Detection is a methodological framework within fraud detection that identifies and verifies deceptive assertions in structured or unstructured data by distinguishing factually unsupported or intentionally falsified statements from legitimate claims through analytical, statistical, or machine learning techniques.

130

Publications

8.7K

Citations

431

Authors

195

Institutions

Publications per year

2017–2026

86

Authors

431

  1. DW
    Dong Wang

    University of Notre Dame, United States

    4 publications · 204 citations

    4

    h-index

  2. KP
    Kashyap Popat

    Max Planck Institute for Informatics, Germany

    4 publications · 707 citations

    4

    h-index

  3. GW
    Gerhard Weikum

    Max Planck Institute for Informatics, Germany

    4 publications · 707 citations

    4

    h-index

  4. UK
    Ullrich K. H. Ecker

    The University of Western Australia, Australia

    4 publications · 292 citations

    4

    h-index

  5. SM
    Subhabrata Mukherjee

    Max Planck Institute for Informatics, Germany

    4 publications · 707 citations

    4

    h-index

1–5 of 431

Venues

  1. SSRN Electronic Journal

    Repository · 4 publications · 75 citations

    4

    h-index

  2. arXiv (Cornell University)

    Repository · 4 publications · 117 citations

    4

    h-index

  3. Harvard Kennedy School Misinformation Review

    3 publications · 273 citations

    3

    h-index

  4. PubMed

    Repository · 3 publications · 80 citations

    3

    h-index

  5. IEEE Transactions on Knowledge and Data Engineering

    TKDE · 2 publications · 44 citations

    2

    h-index

1–5