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Detection of Induced Activity in Social Networks: Model and Methodology

Author

Listed:
  • Dmitrii Gavra

    (Department of Public Relations in Business, St. Petersburg State University, 7-9 Universitetskaya Embankment, 199034 St. Petersburg, Russia)

  • Ksenia Namyatova

    (Department of Public Relations in Business, St. Petersburg State University, 7-9 Universitetskaya Embankment, 199034 St. Petersburg, Russia)

  • Lidia Vitkova

    (St. Petersburg Federal Research Center of the Russian Academy of Sciences (SPC RAS), 39, 14th Line V.O., 199178 St. Petersburg, Russia)

Abstract

This paper examines the problem of social media special operations and especially induced support in social media during political election campaigns. The theoretical background of the paper is based on the study fake activity in social networks during pre-election processes and the existing models and methods of detection of such activity. The article proposes a methodology for identifying and diagnosing induced support for a political project. The methodology includes a model of induced activity, an algorithm for segmenting the audience of a political project, and a technique for detecting and diagnosing induced support. The proposed methodology provides identification of network combatants, participants of social media special operations, influencing public opinion in the interests of a political project. The methodology can be used to raise awareness of the electorate, the public, and civil society in general about the presence of artificial activity on the page of a political project.

Suggested Citation

  • Dmitrii Gavra & Ksenia Namyatova & Lidia Vitkova, 2021. "Detection of Induced Activity in Social Networks: Model and Methodology," Future Internet, MDPI, vol. 13(11), pages 1-13, November.
    • Handle: RePEc:gam:jftint:v:13:y:2021:i:11:p:297-:d:684787
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    References listed on IDEAS

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    1. Frank M. Bass, 2004. "A New Product Growth for Model Consumer Durables," Management Science, INFORMS, vol. 50(12_supple), pages 1825-1832, December.
    2. Cheng, Chun & Luo, Yun & Yu, Changbin, 2020. "Dynamic mechanism of social bots interfering with public opinion in network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 551(C).
    3. Michael MacKuen & Jennifer Wolak & Luke Keele & George E. Marcus, 2010. "Civic Engagements: Resolute Partisanship or Reflective Deliberation," American Journal of Political Science, John Wiley & Sons, vol. 54(2), pages 440-458, April.
    4. Anjana Susarla & Jeong-Ha Oh & Yong Tan, 2012. "Social Networks and the Diffusion of User-Generated Content: Evidence from YouTube," Information Systems Research, INFORMS, vol. 23(1), pages 23-41, March.
    5. Frank M. Bass, 2004. "Comments on "A New Product Growth for Model Consumer Durables The Bass Model"," Management Science, INFORMS, vol. 50(12_supple), pages 1833-1840, December.
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    Cited by:

    1. Svetlana S. Bodrunova, 2022. "Editorial for the Special Issue “Selected Papers from the 9th Annual Conference ‘Comparative Media Studies in Today’s World’ (CMSTW’2021)”," Future Internet, MDPI, vol. 14(11), pages 1-3, November.
    2. Peter R. J. Trim & Yang-Im Lee & An Vu, 2023. "Insights into How Vietnamese Retailers Utilize Social Media to Facilitate Knowledge Creation through the Process of Value Co-Creation," Future Internet, MDPI, vol. 15(4), pages 1-18, March.

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