Semi-supervised Bot Detection

Semi-Supervised Bot Detection is a machine learning approach to identifying automated social media accounts that leverages a small set of labeled bot and human examples alongside a larger body of unlabeled data to improve detection accuracy by exploiting underlying data distribution patterns.

12

Publications

597

Citations

46

Authors

27

Institutions

Publications per year

2017–2026

11

Authors

46

  1. KR
    Kim‐Kwang Raymond Choo

    The University of Texas at San Antonio, United States

    1 publications · 72 citations

    1

    h-index

  2. YC
    Yuling Chen

    Guizhou University, China

    1 publications · 33 citations

    1

    h-index

  3. MA
    Malak Aljabri

    Imam Abdulrahman Bin Faisal University, Saudi Arabia

    1 publications · 104 citations

    1

    h-index

  4. RZ
    Rachid Zagrouba

    Imam Abdulrahman Bin Faisal University, Saudi Arabia

    1 publications · 104 citations

    1

    h-index

  5. HX
    Haiyong Xie

    University of Science and Technology of China, China

    1 publications · 61 citations

    1

    h-index

1–5 of 46

Venues

  1. Applied Sciences

    2 publications · 53 citations

    2

    h-index

  2. Big Data

    1 publications · 147 citations

    1

    h-index

  3. Symmetry

    1 publications · 61 citations

    1

    h-index

  4. Social Network Analysis and Mining

    1 publications · 104 citations

    1

    h-index

  5. Journal of Cybersecurity

    JCS · 1 publications · 17 citations

    1

    h-index

1–5