Abstract
The phenomenon known as the “echo chamber” has been widely acknowledged as a significant force affecting society. This has been particularly evident during the Covid-19 pandemic, wherein the echo chamber effect has significantly influenced public responses. Therefore, detecting echo chambers and mitigating their adverse impacts has become crucial to facilitate a more diverse exchange of ideas, fostering a more understanding and empathetic society. In response, we use deep learning methodologies to model each user’s beliefs based on their historical message contents and behaviours. As such, we propose a novel, content-based framework built on the foundation of weighted beliefs. This framework is capable of detecting potential echo chambers by creating user belief graphs, utilizing their historical messages and behaviours. To demonstrate the practicality of this approach, we conducted experiments using the Twitter dataset on Covid-19. These experiments illustrate the potential for individuals to be isolated within echo chambers. Furthermore, our in-depth analysis of the results reveals patterns of echo chamber evolution and highlights the importance of weighted relations. Understanding these patterns can be instrumental in the development of tools and strategies to combat misinformation, encourage the sharing of diverse perspectives, and enhance the collective well-being and social good of our digital society.
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References
Abd-Alrazaq, A., Alhuwail, D., Househ, M., Hamdi, M., Shah, Z., et al.: Top concerns of tweeters during the covid-19 pandemic: infoveillance study. J. Med. Internet Res. 22(4), e19016 (2020)
Alatawi, F., et al.: A survey on echo chambers on social media: description, detection and mitigation. arXiv preprint arXiv:2112.05084 (2021)
Arnaboldi, V., Conti, M., La Gala, M., Passarella, A., Pezzoni, F.: Ego network structure in online social networks and its impact on information diffusion. Comput. Commun. 76, 26–41 (2016)
Bail, C.A., et al.: Exposure to opposing views on social media can increase political polarization. Proc. Natl. Acad. Sci. 115(37), 9216–9221 (2018)
Barberá, P., Jost, J.T., Nagler, J., Tucker, J.A., Bonneau, R.: Tweeting from left to right: is online political communication more than an echo chamber? Psychol. Sci. 26(10), 1531–1542 (2015)
Bruns, A.: Echo chamber? What echo chamber? Reviewing the evidence. In: 6th Biennial Future of Journalism Conference (FOJ 2017) (2017)
Cinelli, M., Morales, G.D.F., Galeazzi, A., Quattrociocchi, W., Starnini, M.: The echo chamber effect on social media. Proc. Natl. Acad. Sci. 118(9) (2021)
Cossard, A., Morales, G.D.F., Kalimeri, K., Mejova, Y., Paolotti, D., Starnini, M.: Falling into the echo chamber: the Italian vaccination debate on twitter. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 130–140 (2020)
Hu, Y., Wu, S., Jiang, C., Li, W., Bai, Q., Roehrer, E.: AI facilitated isolations? The impact of recommendation-based influence diffusion in human society. In: Raedt, L.D. (ed.) Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI 2022, pp. 5080–5086. International Joint Conferences on Artificial Intelligence Organization (2022)
Jamieson, K.H., Cappella, J.N.: Echo Chamber: Rush Limbaugh and the Conservative Media Establishment. Oxford University Press, Oxford (2008)
Jiang, C., D’Arienzo, A., Li, W., Wu, S., Bai, Q.: An operator-based approach for modeling influence diffusion in complex social networks. J. Soc. Comput. 2(2), 166–182 (2021)
Li, W., Bai, Q., Jiang, C., Zhang, M.: Stigmergy-based influence maximization in social networks. In: Booth, R., Zhang, M.-L. (eds.) PRICAI 2016. LNCS (LNAI), vol. 9810, pp. 750–762. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-42911-3_63
Li, W., Bai, Q., Zhang, M.: Agent-based influence propagation in social networks. In: 2016 IEEE International Conference on Agents (ICA), pp. 51–56. IEEE (2016)
Li, W., Bai, Q., Zhang, M.: Siminer: a stigmergy-based model for mining influential nodes in dynamic social networks. IEEE Trans. Big Data 5(2), 223–237 (2018)
Lwin, M.O., et al.: Global sentiments surrounding the covid-19 pandemic on twitter: analysis of twitter trends. JMIR Public Health Surveill. 6(2), e19447 (2020)
Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space (2013)
Morini, V., Pollacci, L., Rossetti, G.: Toward a standard approach for echo chamber detection: reddit case study. Appl. Sci. 11(12), 5390 (2021)
Romer, D., Jamieson, K.H.: Patterns of media use, strength of belief in covid-19 conspiracy theories, and the prevention of covid-19 from march to July 2020 in the united states: survey study. J. Med. Internet Res. 23(4), e25215 (2021)
Shi, J., et al.: Automated concern exploration in pandemic situations - COVID-19 as a use case. In: Uehara, H., Yamaguchi, T., Bai, Q. (eds.) PKAW 2021. LNCS (LNAI), vol. 12280, pp. 178–185. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-69886-7_15
Veličković, P., Casanova, A., Lió, P., Cucurull, G., Romero, A., Bengio, Y.: Graph attention networks (2018)
Villa, G., Pasi, G., Viviani, M.: Echo chamber detection and analysis. Soc. Netw. Anal. Min. 11(1), 1–17 (2021)
Xue, J., Chen, J., Chen, C., Zheng, C., Li, S., Zhu, T.: Public discourse and sentiment during the covid 19 pandemic: using latent dirichlet allocation for topic modeling on twitter. PLoS ONE 15(9), e0239441 (2020)
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Wang, G., Li, W., Wu, S., Bai, Q., Lai, E.MK. (2024). BeECD: Belief-Aware Echo Chamber Detection over Twitter Stream. In: Liu, F., Sadanandan, A.A., Pham, D.N., Mursanto, P., Lukose, D. (eds) PRICAI 2023: Trends in Artificial Intelligence. PRICAI 2023. Lecture Notes in Computer Science(), vol 14327. Springer, Singapore. https://doi.org/10.1007/978-981-99-7025-4_27
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DOI: https://doi.org/10.1007/978-981-99-7025-4_27
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