single-jc.php

JACIII Vol.30 No.3 pp. 689-702
(2026)

Research Paper:

Influence of Supporters’ Purposeful Movement on Effectiveness of Propaganda

Jiateng Pan*,† ORCID Icon, Atsushi Yoshikawa*,** ORCID Icon, and Masayuki Yamamura*

*School of Computing, Institute of Science Tokyo
4259 Nagatsuta-cho, Midori-ku, Yokohama, Kanagawa 226-8501, Japan

Corresponding author

**College of Science and Engineering, Kanto Gakuin University
1-50-1 Mutsuura-higashi, Kanazawa-ku, Yokohama 236-8501, Japan

Received:
June 30, 2025
Accepted:
December 2, 2025
Published:
May 20, 2026
Keywords:
propaganda, bandwagon effect, agent-based simulation, clustering, Schelling model
Abstract

Conventional wisdom holds that growing a supporter base strengthens propaganda via the bandwagon effect; yet, real-world outcomes often show the opposite. We investigate why larger bases can coincide with lower conversion rates among undecided individuals. Using an agent-based simulation that incorporates a Schelling-style movement rule, we run nine scenarios across three competitive regimes (balanced, competitor-strong, propagandist-strong) with supporter shares ranging from 10%–33%. We find: (1) purposeful movement (preference-driven relocation) induces spatial clustering that reduces cross-type exposure, leading clustered supporters to consistently underperform dispersed supporters; (2) when supporters on both sides exhibit purposeful movement, a numerically inferior side could gain an advantage under certain conditions (e.g., when the opponent clusters more); and (3) excessive increases in supporter numbers—which promote strong spatial clustering—can erode a dominant propagandist’s edge. The mechanism is straightforward: clustering satisfies in-group preferences but isolates supporters from the undecided, muting the bandwagon effect. These results qualify the presumed linear link between supporter volume and campaign success and offer practical guidance: prioritize maintaining supporter dispersion over simply maximizing numbers (e.g., broad geographic reach rather than concentrated rallies). Our framework is reproducible and points to extensions involving heterogeneous agents, nonlinear persuasion, and diverse network topologies.

Purposeful movement reduces support rate

Purposeful movement reduces support rate

Cite this article as:
J. Pan, A. Yoshikawa, and M. Yamamura, “Influence of Supporters’ Purposeful Movement on Effectiveness of Propaganda,” J. Adv. Comput. Intell. Intell. Inform., Vol.30 No.3, pp. 689-702, 2026.
Data files:
References
  1. [1] E. Bakshy, J. M. Hofman, W. A. Mason, and D. J. Watts, “Everyone’s an influencer: quantifying influence on twitter,” Proc. Fourth ACM Int. Conf. Web Search Data Min. (WSDM’11), pp. 65-74, 2011. https://doi.org/10.1145/1935826.1935845
  2. [2] R. Schmitt-Beck, “Bandwagon effect,” G. Mazzoleni (Ed.), “The International Encyclopedia of Political Communication,” John Wiley & Sons, pp. 1-5, 2015. https://doi.org/10.1002/9781118541555.wbiepc015
  3. [3] S.-M. Choi, H. Lee, Y.-S. Han, K. L. Man, and W. K. Chong, “A recommendation model using the bandwagon effect for e-marketing purposes in IoT,” Int. J. Distrib. Sens. Netw., Vol.11, Issue 7, Article No.475163, 2015. https://doi.org/10.1155/2015/47516
  4. [4] X. Shen, “The role of psychological needs in understanding propaganda’s heterogeneous effects,” Social Science Research Network, 2022. https://doi.org/10.2139/ssrn.4177261
  5. [5] R. M. Chang, W. Oh, A. Pinsonneault, and D. Kwon, “A network perspective of digital competition in online advertising industries: A simulation-based approach,” Inf. Syst. Res., Vol.21, Issue 3, pp. 571-593, 2010. https://doi.org/10.1287/isre.1100.0302
  6. [6] Gallup, “Party affiliation.” https://news.gallup.com/poll/15370/party-affiliation.aspx [Accessed May 28, 2024]
  7. [7] D. E. Broockman and J. L. Kalla, “When and why are campaigns’ persuasive effects small? Evidence from the 2020 U.S. presidential election,” Am. J. Political Sci., Vol.67, Issue 4, pp. 833-849, 2023. https://doi.org/10.1111/ajps.12724
  8. [8] D. Gambetta, G. Mauro, and L. Pappalardo, “Mobility constraints in segregation models,” Scient. Rep., Vol.13, Article No.12087, 2023. https://doi.org/10.1038/s41598-023-38519-6
  9. [9] Y. Liao, J. Gil, S. Yeh, R. H. M. Pereira, and L. Alessandretti, “Socio-spatial segregation and human mobility: A review of empirical evidence,” arXiv:2403.06641, 2024. https://arxiv.org/abs/2403.06641
  10. [10] Y. Zhou and L. Liu, “Social influence based clustering of heterogeneous information networks,” Proc. 19th ACM SIGKDD Int. Conf. Knowl. Discovery Data Min., pp. 338-346, 2013. https://doi.org/10.1145/2487575.2487640
  11. [11] R. B. Cialdini and N. J. Goldstein, “Social influence: Compliance and conformity,” Annu. Rev. Psychol., Vol.55, No.2, pp. 591-621, 2004. https://doi.org/10.1146/annurev.psych.55.090902.142015
  12. [12] M. Van Zomeren, T. Postmes, and R. Spears, “Toward an integrative social identity model of collective action: A quantitative research synthesis of three socio-psychological perspectives,” Psychol. Bull., Vol.134, Issue 4, pp. 504-535, 2008. https://doi.org/10.1037/0033-2909.134.4.504
  13. [13] C. Castellano, S. Fortunato, and V. Loreto, “Statistical physics of social dynamics,” Rev. Mod. Phys., Vol.81, No.2, pp. 591-646, 2009. https://doi.org/10.1103/RevModPhys.81.591
  14. [14] M. Jalili and M. Perc, “Information cascades in complex networks,” J. Complex Netw., Vol.5, Issue 5, pp. 665-693, 2017. https://doi.org/10.1093/comnet/cnx019
  15. [15] S. Bikhchandani, D. Hirshleifer, and I. Welch, “A theory of fads, fashion, custom, and cultural change as informational cascades,” J. Polit. Econ., Vol.100, No.5, pp. 992-1026, 1992. https://doi.org/10.1086/261849
  16. [16] C. Baraniuk, “Covid-19: People are gathering again, but can crowds be made safe?,” BMJ, Vol.371, Article No.m3511, 2020. https://doi.org/10.1136/bmj.m3511
  17. [17] A. Tavan, A. D. Tafti, M. Nekoie-Moghadam, M. Ehrampoush, M. R. V. Nasab, H. Tavangar, and H. Fallahzadeh, “Risks threatening the health of people participating in mass gatherings: A systematic review,” J. Educ. Health Promot., Vol.8, Article No.209, 2019. https://doi.org/10.4103/jehp.jehp_214_19
  18. [18] P. Zhu, X. Tan, M. Wang, F. Guo, S. Shi, and Z. Li, “The impact of mass gatherings on the local transmission of COVID-19 and the implications for social distancing policies: Evidence from Hong Kong,” PloS One, Vol.18, Issue 2, Article No.e0279539, 2023. https://doi.org/10.1371/journal.pone.0279539
  19. [19] T. C. Schelling, “Dynamic models of segregation,” J. Math. Sociol., Vol.1, Issue 2, pp. 143-186, 1971. https://doi.org/10.1080/0022250X.1971.9989794
  20. [20] D. Vinković and A. Kirman, “A physical analogue of the Schelling model,” Proc. Natl. Acad. Sci., Vol.103, Issue 51, pp. 19261-19265, 2006. https://doi.org/10.1073/pnas.0609371103
  21. [21] N. G. Domic, E. Goles, and S. Rica, “Dynamics and complexity of the Schelling segregation model,” Phys. Rev. E, Vol.83, Article No.056111, 2011. https://doi.org/10.1103/PhysRevE.83.056111
  22. [22] W. Michener, “The individual psychology of group hate,” J. Hate Stud., Vol.10, Issue 1, 2011. https://doi.org/10.33972/jhs.112
  23. [23] A. Singh, D. Vainchtein, and H. Weiss, “Schelling’s segregation model: parameters, scaling, and aggregation,” Demogr. Res., Vol.21, pp. 341-366, 2009. https://doi.org/10.4054/DemRes.2009.21.12
  24. [24] W. C. Kim, “Blue ocean strategy: From theory to practice,” Calif. Manag. Rev., Vol.47, Issue 3, pp. 105-121, 2005. https://doi.org/10.1177/000812560504700301

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on May. 20, 2026