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JRM Vol.36 No.3 pp. 555-567
doi: 10.20965/jrm.2024.p0555
(2024)

Paper:

Preventing the Diffusion of Disinformation on Disaster SNS by Collective Debunking with Penalties

Masao Kubo*, Hiroshi Sato* ORCID Icon, Saori Iwanaga** ORCID Icon, and Akihiro Yamaguchi***

*Department of Computer Science, National Defense Academy of Japan
1-10-20 Hashirimizu, Yokosuka, Kanagawa 239-8686, Japan

**Japan Coast Guard Academy
5-1 Wakaba-cho, Kure, Hiroshima 737-8512, Japan

***Department of Information and Systems Engineering, Fukuoka Institute of Technology
3-30-1 Wajiro-Higashi, Higashi-ku, Fukuoka 811-0295, Japan

Received:
November 22, 2023
Accepted:
February 13, 2024
Published:
June 20, 2024
Keywords:
social media dynamics, fact check, social deception, evolutionary game, information disorder
Abstract

As online resources such as social media are increasingly used in disaster situations, confusion caused by the spread of false information, misinformation, and hoaxes has become an issue. Although a large amount of research has been conducted on how to suppress disinformation, i.e., the widespread dissemination of such false information, most of the research from a revenue perspective has been based on prisoner’s dilemma experiments, and there has been no analysis of measures to deal with the actual occurrence of disinformation on disaster SNSs. In this paper, we focus on the fact that one of the characteristics of disaster SNS information is that it allows citizens to confirm the reality of a disaster. Hereafter, we refer to this as collective debunking, and we propose a profit-agent model for it and conduct an analysis using an evolutionary game. As a result, we experimentally found that deception in the confirmation of disaster information uploaded to SNS is likely to lead to the occurrence of disinformation. We also found that if this deception can be detected and punished, for example by patrols, it tends to suppress the occurrence of disinformation.

A disaster SNS with collective debunking

A disaster SNS with collective debunking

Cite this article as:
M. Kubo, H. Sato, S. Iwanaga, and A. Yamaguchi, “Preventing the Diffusion of Disinformation on Disaster SNS by Collective Debunking with Penalties,” J. Robot. Mechatron., Vol.36 No.3, pp. 555-567, 2024.
Data files:
References
  1. [1] L. Palen, S. Vieweg, and K. M. Anderson, “Supporting ‘everyday analysts’ in safety-and time-critical situations,” The Information Society, Vol.27, Issue 1, pp. 52-62, 2011. https://doi.org/10.1080/01972243.2011.534370
  2. [2] P. Agarwal, R. A. Aziz, and J. Zhuang, “Interplay of rumor propagation and clarification on social media during crisis events – A game-theoretic approach,” European J. of Operational Research, Vol.298, Issue 2, pp. 714-733, 2022. https://doi.org/10.1016/j.ejor.2021.06.060
  3. [3] S. T. Muhammed and S. K. Mathew, “The disaster of misinformation: a review of research in social media,” Int. J. of Data Science and Analytics, Vol.13, No.4, pp. 271-285, 2022. https://doi.org/10.1007%2Fs41060-022-00311-6
  4. [4] M. Miyabe, A. Nadamoto, and E. Aramaki, “Development of Service for Prevention of Spreading of False Rumors based on Rumor-correction Information,” Trans. of Information Processing Society of Japan, Vol.55, No.1, pp. 563-573, 2014 (in japanese).
  5. [5] M. Cheng, C. Yin, J. Zhang, S. Nazarian, J. Deshmukh, and P. Bogdan, “A general trust framework for multi-agent systems,” Proc. of the 20th Int. Conf. on Autonomous Agents and Multiagent Systems, pp. 332-340, 2021.
  6. [6] Y. Hirahara, F. Toriumi, and T. Sugawara, “Cooperation-dominant Situations in Meta-rewards Games on WS- and BA-model Networks,” Computer Software, Vol.31, No.3, pp. 211-221, 2014 (in Japanese).
  7. [7] R. Iizuka, F. Toriumi, M. Nishiguchi, M. Takano, and M. Yoshida, “Impact of correcting misinformation on social disruption,” PLoS ONE, Vol.17, No.4, Article No.e0265734, 2022. https://doi.org/10.1371/journal.pone.0265734
  8. [8] K. Ikeda, T. Sakaki, F. Toriumi, and S. Kurihara, “Proposal of Information Diffusion Model Focusing on Word-of-mouth Propagation and Validation of Suppressing Methods,” IPSJ TOM, Vol.11, No.1, pp. 21-36, 2018 (in Japanese).
  9. [9] Y. Okada, K. Ikeda, K. Shinoda, F. Toriumi, T. Sakaki, K. Kazama, M. Numao, I. Noda, and S. Kurihara, “SIR-Extended Information Diffusion Model of False Rumor and its Prevention Strategy for Twitter,” J. Adv. Comput. Intell. Intell. Inform., Vol.18, No.4, pp. 598-607, 2014. https://doi.org/10.20965/jaciii.2014.p0598
  10. [10] C. Kopp, K. B. Korb, and B. I. Mills, “Information-theoretic models of deception: Modelling cooperation and diffusion in populations exposed to ‘fake news’,” PLoS ONE, Vol.13, No.11, Article No.e0207383, 2018. https://doi.org/10.1371/journal.pone.0207383
  11. [11] Z. Guo, J.-H. Cho, I.-R. Chen, S. Sengupta, M. Hong, and T. Mitra, “Online Social Deception and Its Countermeasures: A Survey,” IEEE Access, Vol.9, pp. 1770-1806, 2021. https://doi.org/10.1109/ACCESS.2020.3047337
  12. [12] P. Meel and D. K. Vishwakarma, “Fake news, rumor, information pollution in social media and web: A contemporary survey of state-of-the-arts, challenges and opportunities,” Expert Systems with Applications, Vol.153, Article No.112986, 2020.
  13. [13] C. Wardle and H. Derakhshan, “Information disorder: Toward an interdisciplinary framework for research and policy making,” Council of Europe, 2017.
  14. [14] A. Bergh, “Social network centric warfare – Understanding influence operations in social media,” 2019.
  15. [15] M. A. Nowak, “Evolutionary dynamics: Exploring the equations of life,” Harvard University Press, 2006.
  16. [16] A. Y. K. Chua, S.-M. Cheah, D. H. Goh, and E.-P. Lim, “Collective rumor correction on the death hoax of a political figure in social media,” Pacific Asia Conf. on Information Systems, 2016.
  17. [17] T. Shibutani, “Improvised News: A Sociological Study of Rumor,” The Bobbs-Merrill Company Inc., 1966.
  18. [18] S. Vosoughi, D. Roy, and S. Aral, “The spread of true and false news online,” Science, Vol.359, Issue 6380, pp. 1146-1151, 2018. https://doi.org/10.1126/science.aap9559
  19. [19] G. W. Allport and L. Postman, “The Psychology of Rumor,” Henry Holt and Company, 1947.
  20. [20] O. Oh, M. Agrawal, and H. R. Rao, “Community intelligence and social media services: A rumor theoretic analysis of tweets during social crises,” MIS Quarterly, Vol.37, No.2, pp. 407-442, 2013.
  21. [21] L. Palen, A. L. Hughes, and S. Peterson, “Chapter 11: Social Media and Emergency Management,” J. E. Trainor and T. Subbio (Eds.), Critical Issues in Disaster Science and Management, pp. 349-392, FEMA Higher Education Project, 2014.
  22. [22] D. Murungi, S. Purao, and D. Yates, “Beyond Facts: A New Spin on Fake News in the Age of Social Media,” Proc. of AMCIS 2018, 2018. https://aisel.aisnet.org/amcis2018/VirtualCC/Presentations/12
  23. [23] Y. Chi, S. Zhu, K. Hino, Y. Gong, and Y. Zhang, “iOLAP: A Framework for Analyzing the Internet, Social Networks, and Other Networked Data,” IEEE Trans. on Multimedia, Vol.11, Issue 3, pp. 372-382, 2009.
  24. [24] N. S. Kovach, A. S. Gibson, and G. B. Lamont, “Hypergame theory: a model for conflict, misperception, and deception,” Game Theory, Vol.2015, Article 570639, 2015. https://doi.org/10.1155/2015/570639
  25. [25] I. Greenberg, “The Role of Deception in Decision Theory,” J. of Conflict Resolution, Vol.26, No.1, pp. 139-156, 1982. https://doi.org/10.1177/0022002782026001005
  26. [26] A. Borden, “What is information warfare?,” Aerospace Power Chronicles, 1999.
  27. [27] M. Kubo, K. Naruse, H. Sato, and T. Matubara, “The Possibility of an Epidemic Meme Analogy for Web Community Population Analysis,” H. Yin, P. Tino, E. Corchado, W. Byrne, and X. Yao (Eds.), “Intelligent Data Engineering and Automated Learning – IDEAL 2007,” Lecture Notes in Computer Science, Vol.4881, pp. 1073-1080, 2007. https://doi.org/10.1007/978-3-540-77226-2_107
  28. [28] D. Li and J. B. Cruz Jr., “Information, Decision-making and Deception in Games,” Decision Support Systems, Vol.47, Issue 4, pp. 518-527, 2009. https://doi.org/10.1016/j.dss.2009.05.001
  29. [29] M. Matsumoto, “Relationship between Norm-internalization and Cooperation in N-person Prisoners’ Dilemma Game,” Trans. of the Japanese Society for Artificial Intelligence, Vol.21, No.2, pp. 167-175, 2006 (in Japanese). https://doi.org/10.1527/tjsai.21.167
  30. [30] R. Axelrod, “An Evolutionary Approach to Norms,” American Political Science Review, Vol.80, No.4, pp. 1095-1111, 1986.
  31. [31] H. Yamamoto and I. Okada, “Evolution of Cooperation by a Social Vaccine,” The IEICE Trans. on Information and System, Vol.94, No.11, pp. 1836-1846, 2011 (in Japanese).
  32. [32] F. Toriumi and H. Yamamoto, “Evolutional Cooperation on Social Media,” IPSJ J., Vol.53, No.11, pp. 2507-2515, 2012.

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