Web Spam Detection

Web Spam Detection is a computational method in the field of Web Mining and Web Search that identifies deceptive or manipulative web content designed to exploit search engine algorithms, distinguishing itself from general content classification by focusing specifically on detecting pages created solely for artificially inflating search rankings through irrelevant, duplicated, or hidden information.

608

Publications

37.8K

Citations

1.6K

Authors

542

Institutions

Publications per year

2017–2026

179

Authors

1.6K

  1. CC
    Carlos Castillo

    Yahoo (Spain), Spain

    11 publications · 1.1K citations

    11

    h-index

  2. AA
    András A. Benczúr

    Hungarian Academy of Sciences, Hungary

    10 publications · 429 citations

    10

    h-index

  3. CP
    Calton Pu

    Georgia Institute of Technology, United States

    9 publications · 437 citations

    9

    h-index

  4. JC
    James Caverlee

    Texas A&M University, United States

    8 publications · 353 citations

    8

    h-index

  5. YL
    Yiqun Liu

    Tsinghua University, China

    8 publications · 222 citations

    8

    h-index

1–5 of 1.6K

Venues

  1. Expert Systems with Applications

    14 publications · 643 citations

    14

    h-index

  2. International Journal of Computer Applications

    8 publications · 476 citations

    8

    h-index

  3. arXiv (Cornell University)

    Repository · 7 publications · 407 citations

    7

    h-index

  4. ACM Transactions on the Web

    TWEB · 7 publications · 430 citations

    7

    h-index

  5. Knowledge-Based Systems

    7 publications · 975 citations

    7

    h-index

1–5