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Social media actors: perception and optimization of influence across different types

Author

Listed:
  • Alexander A. Kharlamov

    (RAS
    Higher School of Economic
    Moscow Institute of Physics and Technology
    Moscow State Linguistic University)

  • Aleksey N. Raskhodchikov

    (Moscow Center of Urban Studies ‘City’)

  • Maria Pilgun

    (Lomonosov Moscow State University
    Russian State Social University)

Abstract

The paper deals with the analysis of the communicative behavior of various types of actors, speech perception and optimization of influence based on social media data and is an extended version of the report presented at CSoNet 2020 and published based on the deliverables of the conference. The paper proposes an improved methodology that is tested on the new material of conflicts regarding urban planning. The research was conducted on the material of social media concerning the construction of the South-East Chord in Moscow (Russia). The study involved a cross-disciplinary approach using neural network technologies, complex networks analysis. The dataset included social networks, microblogs, forums, blogs, videos, reviews. This paper presents the semantic model for the influence maximization analysis in social networks using neural network technologies, also proposed a variant of analyzing the situation with individual and collective actors, multiple opinion leaders, with a dynamic transformation of the hierarchy and ratings according to various parameters.

Suggested Citation

  • Alexander A. Kharlamov & Aleksey N. Raskhodchikov & Maria Pilgun, 2025. "Social media actors: perception and optimization of influence across different types," Journal of Combinatorial Optimization, Springer, vol. 49(2), pages 1-39, March.
    • Handle: RePEc:spr:jcomop:v:49:y:2025:i:2:d:10.1007_s10878-024-01238-3
    DOI: 10.1007/s10878-024-01238-3
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    References listed on IDEAS

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    1. Robert M. Bond & Christopher J. Fariss & Jason J. Jones & Adam D. I. Kramer & Cameron Marlow & Jaime E. Settle & James H. Fowler, 2012. "A 61-million-person experiment in social influence and political mobilization," Nature, Nature, vol. 489(7415), pages 295-298, September.
    2. Wenguo Yang & Shengminjie Chen & Suixiang Gao & Ruidong Yan, 2020. "Boosting node activity by recommendations in social networks," Journal of Combinatorial Optimization, Springer, vol. 40(3), pages 825-847, October.
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