Duplicate Content Detection

Duplicate Content Detection is a computational method designed to identify substantively similar or verbatim instances of web content across digital sources, distinguishing itself from related content analysis techniques through its specific focus on detecting exact or near-identical replications that may affect search engine indexing and ranking.

104

Publications

5.1K

Citations

331

Authors

146

Institutions

Publications per year

2017–2026

26

Authors

331

  1. ZB
    Ziv Bar-Yossef

    Technion – Israel Institute of Technology, Israel

    3 publications · 130 citations

    3

    h-index

  2. US
    Uri Schonfeld

    University of California, Los Angeles, United States

    3 publications · 130 citations

    3

    h-index

  3. NS
    Narayanan Shivakumar

    Stanford University, United States

    3 publications · 356 citations

    3

    h-index

  4. MA
    Marcos André Gonçalves

    Universidade Federal de Minas Gerais, Brazil

    3 publications · 137 citations

    3

    h-index

  5. WL
    Wei Lai

    Swinburne University of Technology, Australia

    3 publications · 134 citations

    3

    h-index

1–5 of 331

Venues

  1. IEEE Transactions on Knowledge and Data Engineering

    TKDE · 4 publications · 210 citations

    4

    h-index

  2. Information and Software Technology

    3 publications · 135 citations

    3

    h-index

  3. arXiv (Cornell University)

    Repository · 2 publications · 60 citations

    2

    h-index

  4. TRECVID

    2 publications · 40 citations

    2

    h-index

  5. Online Information Review

    2 publications · 110 citations

    2

    h-index

1–5