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International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation

SBP-BRiMS 2019: Social, Cultural, and Behavioral Modeling pp 224–233Cite as

Examining Intensive Groups in YouTube Commenter Networks

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11549))

Abstract

Focal structures are the sets of individuals in social networks that are not influential on their own but are influential collectively. These individuals, when coordinating, can be responsible for massive information diffusion, influence operations, or could coordinate (cyber)-attacks. These communities have high tension than other communities in the social network and can mobilize crowds. In this research, we propose a two-level decomposition optimization method for identifying these intensive groups in the complex social networks by constructing a two-level optimization problem for maximizing the local individual’s degree centrality values and the global modularity measures. We also demonstrate the assembled centrality modularity method by applying to a network of YouTube users commenting on conspiracy theory videos to identify coordinating commenters. The dataset consisted of 9,661 users commenting on 4,145 conspiracy theory videos and the derived commenter network contained more than 4.4 million edges. Focal structure analysis was applied to this network to identify sets of users that are coordinating to promote disinformation dissemination. Our proposed model identifies smallest atomic units having high influence, interactions, higher reachability for information propagation. A multi-criteria optimization problem is also employed to rank the identified sets for further investigations.

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Acknowledgment

This research is funded in part by the U.S. National Science Foundation (IIS-1636933, ACI-1429160, and IIS-1110868), U.S. Office of Naval Research (N00014-10-1-0091, N00014-14-1-0489, N00014-15-P-1187, N00014-16-1-2016, N00014-16-1-2412, N00014-17-1-2605, N00014-17-1-2675), U.S. Air Force Research Lab, U.S. Army Research Office (W911NF-16-1-0189), U.S. Defense Advanced Research Projects Agency (W31P4Q-17-C-0059), Arkansas Research Alliance, and the Jerry L. Maulden/Entergy Foundation at the University of Arkansas, Little Rock. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the funding organizations.

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Authors and Affiliations

  1. University of Arkansas, Little Rock, AR, USA

    Mustafa Alassad, Nitin Agarwal & Muhammad Nihal Hussain

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  1. Mustafa Alassad
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  2. Nitin Agarwal
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  3. Muhammad Nihal Hussain
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Corresponding author

Correspondence to Mustafa Alassad .

Editor information

Editors and Affiliations

  1. United States Military Academy, West Point, NY, USA

    Robert Thomson

  2. University of Michigan–Flint, Flint, MI, USA

    Halil Bisgin

  3. Bucknell University, Lewisburg, PA, USA

    Christopher Dancy

  4. The Ohio State University, Columbus, OH, USA

    Ayaz Hyder

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Alassad, M., Agarwal, N., Hussain, M.N. (2019). Examining Intensive Groups in YouTube Commenter Networks. In: Thomson, R., Bisgin, H., Dancy, C., Hyder, A. (eds) Social, Cultural, and Behavioral Modeling. SBP-BRiMS 2019. Lecture Notes in Computer Science(), vol 11549. Springer, Cham. https://doi.org/10.1007/978-3-030-21741-9_23

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  • DOI: https://doi.org/10.1007/978-3-030-21741-9_23

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-21740-2

  • Online ISBN: 978-3-030-21741-9

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