Clickbait Detection

Clickbait Detection is a computational method designed to identify online content that employs misleading or exaggerated headlines to attract user clicks, distinguished from related misinformation detection techniques by its specific focus on manipulative engagement tactics rather than factual inaccuracy.

80

Publications

4.6K

Citations

234

Authors

109

Institutions

Publications per year

2017–2026

74

Authors

234

  1. MH
    Matthias Hagen

    Bauhaus-Universität Weimar, Germany

    3 publications · 125 citations

    3

    h-index

  2. AJ
    Atishay Jain

    Syracuse University, United States

    3 publications · 318 citations

    3

    h-index

  3. VV
    Vasudeva Varma

    International Institute of Information Technology, Hyderabad, India

    3 publications · 87 citations

    3

    h-index

  4. TG
    Tim Gollub

    Bauhaus-Universität Weimar, Germany

    3 publications · 125 citations

    3

    h-index

  5. MP
    Martin Potthast

    Bauhaus-Universität Weimar, Germany

    3 publications · 125 citations

    3

    h-index

1–5 of 234

Venues

  1. arXiv (Cornell University)

    Repository · 14 publications · 511 citations

    13

    h-index

  2. IEEE Access

    3 publications · 115 citations

    3

    h-index

  3. Applied Sciences

    3 publications · 74 citations

    3

    h-index

  4. Knowledge-Based Systems

    3 publications · 97 citations

    3

    h-index

  5. Procedia Computer Science

    2 publications · 380 citations

    2

    h-index

1–5