2024
Journal article
Open Access
Trends and topics: characterizing echo chambers’ topological stability and in-group attitudes
Cau E., Morini V., Rossetti G.
Computer Science - Social and Information Networks Echo chambers FOS: Computer and information sciences Social media Social and Information Networks (cs.SI)
Nowadays, online debates focusing on a wide spectrum of topics are often characterized by clashes of polarized communities, each fiercely supporting a specific stance. Such debates are sometimes fueled by the presence of echo chambers, insulated systems whose users’ opinions are exacerbated due to the effect of repetition and by the active exclusion of opposite views. This paper offers a framework to explore how echo chambers evolve through time, considering their users’ interaction patterns and the content/attitude they convey while addressing specific controversial issues. The framework is then tested on three Reddit case studies focused on sociopolitical issues (gun control, American politics, and minority discrimination) during the first two years and a half of Donald Trump’s presidency and on an X/Twitter dataset involving BLM discussion tied to the EURO 2020 football championship. Analytical results unveil that polarized users will likely keep their affiliation to echo chambers in time. Moreover, we observed that the attitudes conveyed by Reddit users who joined risky epistemic enclaves are characterized by a slight inclination toward a more negative or neutral attitude when discussing particularly sensitive issues (e.g., fascism, school shootings, or police violence) while X/Twitter ones often tend to express more positive feelings w.r.t. those involved into less polarized communities.
Source: PLOS COMPLEX SYSTEMS, vol. 1 (issue 2)
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[23] Timothy Hospedales, Shaogang Gong, and Tao Xiang. Video behaviour mining using a dynamic topic model. International Journal of Computer Vision, 98(3):303-323, December 2011.
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[26] Christopher E Moody. Mixing dirichlet topic models and word embeddings to make lda2vec, 2016.
[27] Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg Corrado, and Jefrey Dean. Distributed representations of words and phrases and their compositionality. In Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2, NIPS'13, page 3111-3119, Red Hook, NY, USA, 2013. Curran Associates Inc.
[28] David M. Blei and John D. Laferty. Dynamic topic models. In Proceedings of the 23rd international conference on Machine learning - ICML '06. ACM Press, 2006.
[29] Tomoharu Iwata, Shinji Watanabe, Takeshi Yamada, and Naonori Ueda. Topic tracking model for analyzing consumer purchase behavior. In Proceedings of the 21st International Joint Conference on Artificial Intelligence , IJCAI'09, page 1427-1432, San Francisco, CA, USA, 2009. Morgan Kaufmann Publishers Inc.
[30] Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. Bert: Pre-training of deep bidirectional transformers for language understanding, 2018.
[31] Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. Roberta: A robustly optimized bert pretraining approach. ArXiv, abs/1907.11692, 2019.
[32] Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Veselin Stoyanov, and Luke Zettlemoyer. BART: Denoising sequence-to-sequence pretraining for natural language generation, translation, and comprehension. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 7871-7880, Online, July 2020. Association for Computational Linguistics.
[33] Victor Sanh, Lysandre Debut, Julien Chaumond, and Thomas Wolf. Distilbert, a distilled version of bert: smaller, faster, cheaper and lighter. ArXiv, abs/1910.01108, 2019.
[34] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention is all you need. InProceedings of the 31st International Conference on Neural Information Processing Systems, NIPS'17, page 6000-6010, Red Hook, NY, USA, 2017. Curran Associates Inc.
[35] James A. Russell. Core afect and the psychological construction of emotion. Psychological Review, 110(1):145-172, 2003.
[36] Margaret M. Bradley and Peter J. Lang. Afective norms for english words (anew): Instruction manual and afective ratings. 1999.
[37] Amy Beth Warriner, Victor Kuperman, and Marc Brysbaert. Norms of valence, arousal, and dominance for 13, 915 english lemmas. Behavior Research Methods, 45(4):1191-1207, February 2013.
[38] Saif Mohammad. Obtaining reliable human ratings of valence, arousal, and dominance for 20,000 English words. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 174-184, Melbourne, Australia, July 2018. Association for Computational Linguistics.
[39] Jordan J. Louviere, Terry N. Flynn, and A. A. J. Marley. Best-Worst Scaling. Cambridge University Press, September 2015.
[40] Derek Greene, Don´al Doyle, and Pad´raig Cunningham. Tracking the evolution of communities in dynamic social networks. In 2010 International Conference on Advances in Social Networks Analysis and Mining. IEEE, August 2010.
[41] Rajmonda Sulo Caceres, Tanya Berger-Wolf, and Robert Grossman. Temporal scale of processes in dynamic networks. In 2011 IEEE 11th International Conference on Data Mining Workshops. IEEE, December 2011.
[42] Mohamed Salama, Mohamed Ezzeldin, Wael El-Dakhakhni, and Michael Tait. Temporal networks: a review and opportunities for infrastructure simulation. Sustainable and Resilient Infrastructure, 7(1):40-55, February 2020.
[43] Salvatore Citraro and Giulio Rossetti. Identifying and exploiting homogeneous communities in labeled networks. Applied Network Science, 5(1), August 2020.
[44] Vincent D Blondel, Jean-Loup Guillaume, Renaud Lambiotte, and Etienne Lefebvre. Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment, 2008(10):P10008, October 2008.
[45] Maarten Grootendorst. Bertopic: Neural topic modeling with a class-based tf-idf procedure, 2022.
[46] Leland McInnes, John Healy, Nathaniel Saul, and Lukas Großberger. UMAP: Uniform manifold approximation and projection. Journal of Open Source Software, 3(29):861, September 2018.
[47] Richard Bellman. Dynamic programming. Science, 153(3731):34-37, 1966.
[48] Adji B. Dieng, Francisco J. R. Ruiz, and David M. Blei. Topic modeling in embedding spaces. Transactions of the Association for Computational Linguistics, 8:439-453, December 2020.
[49] Gerlof Bouma. Normalized (pointwise) mutual information in collocation extraction. Proceedings of GSCL, 30:31-40, 2009.
[50] Maarten Grootendorst. Keybert: Minimal keyword extraction with bert., 2020.
[51] Virginia Morini, Laura Pollacci, and Giulio Rossetti. Capturing political polarization of reddit submissions in the trump era. In SEBD, pages 80-87, 2020.
[52] Top websites ranking - most visited websites in june 2023.
[53] 1615 L. St NW, Suite 800 Washington, and DC 20036 USA202-419-4300 {\textbar} Main202-857- 8562 {\textbar} Fax202-419-4372 {\textbar} Media Inquiries. 1. partisan divides over political values widen.
[54] Christiane Fellbaum, editor. WordNet: An Electronic Lexical Database. Language, Speech, and Communication. MIT Press, Cambridge, MA, 1998.
[55] S. Lloyd. Least squares quantization in pcm. IEEE Transactions on Information Theory, 28(2):129-137, 1982.
[56] Jaime Carbonell and Jade Goldstein. The use of MMR, diversity-based reranking for reordering documents and producing summaries. In Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval. ACM, August 1998.
[57] Shagun Jhaver, Larry Chan, and Amy Bruckman. The view from the other side: The border between controversial speech and harassment on kotaku in action. First Monday, February 2018.
[58] Adrienne Massanari. #gamergate and the fappening: How reddit's algorithm, governance, and culture support toxic technocultures. New Media and Society, 19(3):329-346, July 2016.
[3] Virginia Morini, Laura Pollacci, and Giulio Rossetti. Toward a standard approach for echo chamber detection: Reddit case study. Applied Sciences, 11(12):5390, June 2021.
[4] Michael Conover, Jacob Ratkiewicz, Matthew Francisco, Bruno Goncalves, Filippo Menczer, and Alessandro Flammini. Political polarization on twitter. Proceedings of the International AAAI Conference on Web and Social Media, 5(1):89-96, August 2021.
[5] Lada A. Adamic and Natalie Glance. The political blogosphere and the 2004 u.s. election. In Proceedings of the 3rd international workshop on Link discovery. ACM, August 2005.
[6] Pedro Guerra, Wagner Meira Jr., Claire Cardie, and Robert Kleinberg. A measure of polarization on social media networks based on community boundaries. Proceedings of the International AAAI Conference on Web and Social Media, 7(1):215-224, August 2021.
[7] Arthur Edwards. (how) do participants in online discussion forums create 'echo chambers' ? Argumentation in political deliberation, 2(1):127-150, May 2013.
[8] E. Gilbert, T. Bergstrom, and K. Karahalios. Blogs are echo chambers: Blogs are echo chambers. In 2009 42nd Hawaii International Conference on System Sciences, pages 1-10, 2009.
[9] Yingqiang Ge, Shuya Zhao, Honglu Zhou, Changhua Pei, Fei Sun, Wenwu Ou, and Yongfeng Zhang. Understanding echo chambers in e-commerce recommender systems. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, July 2020.
[10] Jisun An, Daniele Quercia, and Jon Crowcroft. Partisan sharing. In Proceedings of the second ACM conference on Online social networks. ACM, October 2014.
[11] Eytan Bakshy, Solomon Messing, and Lada A. Adamic. Exposure to ideologically diverse news and opinion on facebook. Science, 348(6239):1130-1132, June 2015.
[12] Fernando H. Caldeorn´, Li-Kai Cheng, Ming-Jen Lin, Yen-Hao Huang, and Yi-Shin Chen. Contentbased echo chamber detection on social media platforms. In Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining. ACM, August 2019.
[13] Kiran Garimella, Gianmarco De Francisci Morales, Aristides Gionis, and Michael Mathioudakis. Political discourse on social media. In Proceedings of the 2018 World Wide Web Conference on World Wide Web - WWW '18. ACM Press, 2018.
[14] Nane Kratzke. How to find orchestrated trolls? a case study on identifying polarized twitter echo chambers. Computers, 12(3):57, March 2023.
[15] Gianmarco De Francisci Morales, Corrado Monti, and Michele Starnini. No echo in the chambers of political interactions on reddit. Scientific Reports , 11(1), February 2021.
[16] Giacomo Villa, Gabriella Pasi, and Marco Viviani. Echo chamber detection and analysis. Social Network Analysis and Mining, 11(1), August 2021.
[17] Matteo Cinelli, Gianmarco De Francisci Morales, Alessandro Galeazzi, Walter Quattrociocchi, and Michele Starnini. The echo chamber efect on social media. Proceedings of the National Academy of Sciences, 118(9), February 2021.
[18] Gergely Palla, Albert-Las´zol´ Barabas´i, and Tamas´ Vicsek. Quantifying social group evolution. Nature, 446(7136):664-667, April 2007.
[19] Remy Cazabet and Giulio Rossetti. Challenges in community discovery on temporal networks. In Computational Social Sciences, pages 181-197. Springer International Publishing, 2019.
[20] Elizaveta Kopacheva and Victoria Yantseva. Users' polarisation in dynamic discussion networks: The case of refugee crisis in sweden. PLOS ONE, 17(2):e0262992, February 2022.
[21] Rob Churchill and Lisa Singh. The evolution of topic modeling. ACM Comput. Surv., 54(10s), nov 2022.
[22] Lin Liu, Lin Tang, Wen Dong, Shaowen Yao, and Wei Zhou. An overview of topic modeling and its current applications in bioinformatics. SpringerPlus, 5(1), September 2016.
[23] Timothy Hospedales, Shaogang Gong, and Tao Xiang. Video behaviour mining using a dynamic topic model. International Journal of Computer Vision, 98(3):303-323, December 2011.
[24] David M. Blei, Andrew Y. Ng, and Michael I. Jordan. Latent dirichlet allocation. J. Mach. Learn. Res., 3(null):993-1022, mar 2003.
[25] Yee Whye Teh, Michael I Jordan, Matthew J Beal, and David M Blei. Hierarchical dirichlet processes. Journal of the American Statistical Association, 101(476):1566-1581, December 2006.
[26] Christopher E Moody. Mixing dirichlet topic models and word embeddings to make lda2vec, 2016.
[27] Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg Corrado, and Jefrey Dean. Distributed representations of words and phrases and their compositionality. In Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2, NIPS'13, page 3111-3119, Red Hook, NY, USA, 2013. Curran Associates Inc.
[28] David M. Blei and John D. Laferty. Dynamic topic models. In Proceedings of the 23rd international conference on Machine learning - ICML '06. ACM Press, 2006.
[29] Tomoharu Iwata, Shinji Watanabe, Takeshi Yamada, and Naonori Ueda. Topic tracking model for analyzing consumer purchase behavior. In Proceedings of the 21st International Joint Conference on Artificial Intelligence , IJCAI'09, page 1427-1432, San Francisco, CA, USA, 2009. Morgan Kaufmann Publishers Inc.
[30] Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. Bert: Pre-training of deep bidirectional transformers for language understanding, 2018.
[31] Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. Roberta: A robustly optimized bert pretraining approach. ArXiv, abs/1907.11692, 2019.
[32] Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Veselin Stoyanov, and Luke Zettlemoyer. BART: Denoising sequence-to-sequence pretraining for natural language generation, translation, and comprehension. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 7871-7880, Online, July 2020. Association for Computational Linguistics.
[33] Victor Sanh, Lysandre Debut, Julien Chaumond, and Thomas Wolf. Distilbert, a distilled version of bert: smaller, faster, cheaper and lighter. ArXiv, abs/1910.01108, 2019.
[34] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention is all you need. InProceedings of the 31st International Conference on Neural Information Processing Systems, NIPS'17, page 6000-6010, Red Hook, NY, USA, 2017. Curran Associates Inc.
[35] James A. Russell. Core afect and the psychological construction of emotion. Psychological Review, 110(1):145-172, 2003.
[36] Margaret M. Bradley and Peter J. Lang. Afective norms for english words (anew): Instruction manual and afective ratings. 1999.
[37] Amy Beth Warriner, Victor Kuperman, and Marc Brysbaert. Norms of valence, arousal, and dominance for 13, 915 english lemmas. Behavior Research Methods, 45(4):1191-1207, February 2013.
[38] Saif Mohammad. Obtaining reliable human ratings of valence, arousal, and dominance for 20,000 English words. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 174-184, Melbourne, Australia, July 2018. Association for Computational Linguistics.
[39] Jordan J. Louviere, Terry N. Flynn, and A. A. J. Marley. Best-Worst Scaling. Cambridge University Press, September 2015.
[40] Derek Greene, Don´al Doyle, and Pad´raig Cunningham. Tracking the evolution of communities in dynamic social networks. In 2010 International Conference on Advances in Social Networks Analysis and Mining. IEEE, August 2010.
[41] Rajmonda Sulo Caceres, Tanya Berger-Wolf, and Robert Grossman. Temporal scale of processes in dynamic networks. In 2011 IEEE 11th International Conference on Data Mining Workshops. IEEE, December 2011.
[42] Mohamed Salama, Mohamed Ezzeldin, Wael El-Dakhakhni, and Michael Tait. Temporal networks: a review and opportunities for infrastructure simulation. Sustainable and Resilient Infrastructure, 7(1):40-55, February 2020.
[43] Salvatore Citraro and Giulio Rossetti. Identifying and exploiting homogeneous communities in labeled networks. Applied Network Science, 5(1), August 2020.
[44] Vincent D Blondel, Jean-Loup Guillaume, Renaud Lambiotte, and Etienne Lefebvre. Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment, 2008(10):P10008, October 2008.
[45] Maarten Grootendorst. Bertopic: Neural topic modeling with a class-based tf-idf procedure, 2022.
[46] Leland McInnes, John Healy, Nathaniel Saul, and Lukas Großberger. UMAP: Uniform manifold approximation and projection. Journal of Open Source Software, 3(29):861, September 2018.
[47] Richard Bellman. Dynamic programming. Science, 153(3731):34-37, 1966.
[48] Adji B. Dieng, Francisco J. R. Ruiz, and David M. Blei. Topic modeling in embedding spaces. Transactions of the Association for Computational Linguistics, 8:439-453, December 2020.
[49] Gerlof Bouma. Normalized (pointwise) mutual information in collocation extraction. Proceedings of GSCL, 30:31-40, 2009.
[50] Maarten Grootendorst. Keybert: Minimal keyword extraction with bert., 2020.
[51] Virginia Morini, Laura Pollacci, and Giulio Rossetti. Capturing political polarization of reddit submissions in the trump era. In SEBD, pages 80-87, 2020.
[52] Top websites ranking - most visited websites in june 2023.
[53] 1615 L. St NW, Suite 800 Washington, and DC 20036 USA202-419-4300 {\textbar} Main202-857- 8562 {\textbar} Fax202-419-4372 {\textbar} Media Inquiries. 1. partisan divides over political values widen.
[54] Christiane Fellbaum, editor. WordNet: An Electronic Lexical Database. Language, Speech, and Communication. MIT Press, Cambridge, MA, 1998.
[55] S. Lloyd. Least squares quantization in pcm. IEEE Transactions on Information Theory, 28(2):129-137, 1982.
[56] Jaime Carbonell and Jade Goldstein. The use of MMR, diversity-based reranking for reordering documents and producing summaries. In Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval. ACM, August 1998.
[57] Shagun Jhaver, Larry Chan, and Amy Bruckman. The view from the other side: The border between controversial speech and harassment on kotaku in action. First Monday, February 2018.
[58] Adrienne Massanari. #gamergate and the fappening: How reddit's algorithm, governance, and culture support toxic technocultures. New Media and Society, 19(3):329-346, July 2016.
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BibTeX entry
@article{oai:iris.cnr.it:20.500.14243/525067,
title = {Trends and topics: characterizing echo chambers’ topological stability and in-group attitudes},
author = {Cau E. and Morini V. and Rossetti G.},
doi = {10.1371/journal.pcsy.0000008 and 10.48550/arxiv.2307.15610},
year = {2024}
}
0000-0002-7692-8134
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