ECO-SA: Optimized Containment Strategy of Propagation in Echo Chambers using Simulated Annealing
Abstract
The social phenomenon of echo chambers is a serious threat to the cybersecurity in social networks, as they foster environments conducive to hate speech and the spread of fake news. Members of an echo chamber tend to ignore attempts to refute the unilateral ideas constantly disseminated within it. In this paper, we propose an optimized containment strategy for the spread of information in echo chambers based on Simulated Annealing (ECO-SA). The proposal identifies critical nodes in graphs, which, when blocked, significantly reduce information diffusion. Simulations on 15 samples of echo chambers, applied to an information diffusion model under different scenarios, show that blocking the strategic nodes identified by ECO-SA reduced the spread to only 45% of the nodes in the network, while the competing method restricts the spread to at least 65% of the nodes.References
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Cota, W., Ferreira, S. C., Pastor-Satorras, R. e Starnini, M. (2019). Quantifying echo chamber effects in information spreading over political communication networks. EPJ Data Science, 8(1):1–13.
de Oliveira, N. R., Medeiros, D. S. e Mattos, D. M. (2020). A sensitive stylistic approach to identify fake news on social networking. IEEE Signal Processing Letters, 27:1250–1254.
de Oliveira, N. R., Medeiros, D. S. e Mattos, D. M. (2021). Caracterização sócio-temporal de conteúdos em redes sociais baseada em processamento em fluxo. Em Anais do XXVI Workshop de Gerência e Operação de Redes e Serviços, p. 54–67. SBC.
Dey, P. e Roy, S. (2017). Centrality based information blocking and influence minimization in online social network. Em 2017 IEEE international conference on advanced networks and telecommunications systems (ANTS), p. 1–6. IEEE.
Donkers, T. e Ziegler, J. (2021). The dual echo chamber: Modeling social media polarization for interventional recommending. Em Fifteenth ACM Conference on Recommender Systems, RecSys ’21, p. 12–22, New York, NY, USA. Association for Computing Machinery.
Holme, P., Kim, B. J., Yoon, C. N. e Han, S. K. (2002). Attack vulnerability of complex networks. Physical review E, 65(5):056109.
Jeon, Y., Kim, B., Xiong, A., Lee, D. e Han, K. (2021). Chamberbreaker: Mitigating the echo chamber effect and supporting information hygiene through a gamified inoculation system. Proceedings of the ACM on Human-Computer Interaction, 5(CSCW2):1–26.
Jeon, Y., Kim, J., Park, S., Ko, Y., Ryu, S., Kim, S.-W. e Han, K. (2024). Hearhere: Mitigating echo chambers in news consumption through an ai-based web system. arXiv preprint arXiv:2402.18222.
Khalil, E., Dilkina, B. e Song, L. (2013). Cuttingedge: Influence minimization in networks. Em Proceedings of Workshop on Frontiers of Network Analysis: Methods, Models, and Applications at NIPS, p. 1–13.
Kim, H., Kim, H., Jo, K. J. e Kim, J. (2021). Starrythoughts: Facilitating diverse opinion exploration on social issues. Proceedings of the ACM on Human-Computer Interaction, 5(CSCW1):1–29.
Kimura, M., Saito, K. e Motoda, H. (2009). Blocking links to minimize contamination spread in a social network. ACM Transactions on Knowledge Discovery from Data (TKDD), 3(2):1–23.
Kirkpatrick, S., Gelatt Jr, C. D. e Vecchi, M. P. (1983). Optimization by simulated annealing. science, 220(4598):671–680.
Lee, Y., Ozer, M., Corman, S. R. e Davulcu, H. (2023). Detecting and measuring the polarization effects of adversarial botnets on twitter. Em Proceedings of the 38th ACM/SIGAPP Symposium on Applied Computing, p. 1641–1649.
Milli, L., Rossetti, G., Pedreschi, D. e Giannotti, F. (2018). Information diffusion in complex networks: The active/passive conundrum. Em Complex Networks & Their Applications VI: Proceedings of Complex Networks 2017 (The Sixth International Conference on Complex Networks and Their Applications), p. 305–313. Springer.
Morini, V., Pollacci, L. e Rossetti, G. (2021). Toward a standard approach for echo chamber detection: Reddit case study. Applied Sciences, 11(12).
Neto, H. N. C., Dusparic, I., Mattos, D. M. e Fernande, N. C. (2022). Fedsa: Accelerating intrusion detection in collaborative environments with federated simulated annealing. Em 2022 IEEE 8th International Conference on Network Softwarization (NetSoft), p. 420–428. IEEE.
Newman, N., Fletcher, R., Schulz, A., Andı, S., Robertson, C. T. e Nielsen, R. K. (2024). Digital news report 2024. Relatório técnico, Reuters Institute for the Study of Journalism.
Pham, C. V., Thai, M. T., Duong, H. V., Bui, B. Q. e Hoang, H. X. (2018). Maximizing misinformation restriction within time and budget constraints. Journal of Combinatorial Optimization, 35(4):1202–1240.
Rossetti, G. e Cazabet, R. (2018). Community discovery in dynamic networks: A survey. ACM Comput. Surv., 51(2).
Shu, K., Sliva, A., Wang, S., Tang, J. e Liu, H. (2017). Fake news detection on social media: A data mining perspective. ACM SIGKDD Explorations Newsletter, 19(1):22–36.
Terren, L. e Borge-Bravo, R. (2021). Echo chambers on social media: a systematic review of the literature. Review of Communication Research, 9:99–118.
Tong, H., Prakash, B. A., Eliassi-Rad, T., Faloutsos, M. e Faloutsos, C. (2012). Gelling, and melting, large graphs by edge manipulation. Em Proceedings of the 21st ACM international conference on Information and knowledge management, p. 245–254.
Xu, M. (2021). Understanding graph embedding methods and their applications. SIAM Review, 63(4):825–853.
Zareie, A. e Sakellariou, R. (2021). Minimizing the spread of misinformation in online social networks: A survey. Journal of Network and Computer Applications, 186:103094.
Zollo, F., Bessi, A., Del Vicario, M., Scala, A., Caldarelli, G., Shekhtman, L., Havlin, S. e Quattrociocchi, W. (2017). Debunking in a world of tribes. PloS one, 12(7):e0181821.
Published
2024-09-16
How to Cite
OLIVEIRA, Nicollas R. de; MEDEIROS, Dianne S. V. de; MATTOS, Diogo M. F..
ECO-SA: Optimized Containment Strategy of Propagation in Echo Chambers using Simulated Annealing. In: BRAZILIAN SYMPOSIUM ON CYBERSECURITY (SBSEG), 24. , 2024, São José dos Campos/SP.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 319-334.
DOI: https://doi.org/10.5753/sbseg.2024.241730.
