Computational Campaigning

Computational Campaigning is a methodological framework within the field of political communication that employs data-driven algorithms, machine learning techniques, and large-scale digital behavior analysis to strategically design, target, and optimize electoral or issue-based campaigns in online environments.

14

Publications

1.4K

Citations

39

Authors

22

Institutions

Publications per year

2017–2026

10

Authors

39

  1. JS
    John Sides

    George Washington University, United States

    1 publications · 14 citations

    1

    h-index

  2. JB
    Jessica Baldwin-Philippi

    Fordham University, United States

    1 publications · 59 citations

    1

    h-index

  3. VL
    Vito Laterza

    University of Cape Town, South Africa

    1 publications · 16 citations

    1

    h-index

  4. TH
    Tahmid Hasan Pranto

    North South University, Bangladesh

    1 publications · 25 citations

    1

    h-index

  5. YY
    Yanwu Yang

    Huazhong University of Science and Technology, China

    1 publications · 61 citations

    1

    h-index

1–5 of 39

Venues

  1. Internet Policy Review

    2 publications · 199 citations

    2

    h-index

  2. PLoS ONE

    1 publications · 16 citations

    1

    h-index

  3. Public Anthropologist

    1 publications · 16 citations

    1

    h-index

  4. 1

    h-index

  5. IET Blockchain

    1 publications · 25 citations

    1

    h-index

1–5