Tim Donkers
ABSTRACT
Echo chambers are social phenomena that amplify agreement and suppress opposing views in social media which may lead to fragmentation and polarization of the user population. In prior research, echo chambers have mainly been modeled as a result of social information diffusion. While most scientific work has framed echo chambers as a result of epistemic imbalances between polarized communities, we argue that members of echo chambers often actively discredit outside sources to maintain coherent world views. We therefore argue that two different types of echo chambers occur in social media contexts: Epistemic echo chambers create information gaps mainly through their structure whereas ideological echo chambers systematically exclude counter-attitudinal information. Diversifying recommendations by simply widening the scope of topics and viewpoints covered to counteract the echo chamber effect may be ineffective in such contexts. To investigate the characteristics of this dual echo chamber view and to assess the depolarizing effects of diversified recommendations, we apply an agent-based modeling approach. We rely on knowledge graph embedding techniques not only to generate recommendations, but also to show how to utilize logical graph queries in embedding spaces to diversify recommendations aimed at challenging polarization in online discussions. The results of our evaluation indicate that counteracting the two different types of echo chambers requires fundamentally different diversification strategies.
Supplemental Material
- Hunt Allcott and Matthew Gentzkow. 2017. Social media and fake news in the 2016 election. Journal of economic perspectives 31, 2 (2017), 211–36.Google Scholar
Cross Ref
- Robert Axelrod. 1997. The dissemination of culture: A model with local convergence and global polarization. Journal of conflict resolution 41, 2 (1997), 203–226. Publisher: Sage Periodicals Press 2455 Teller Road, Thousand Oaks, CA 91320.Google ScholarCross Ref
- Mahsa Badami, Olfa Nasraoui, and Patrick Shafto. 2018. PrCP: Pre-recommendation Counter-Polarization.. In KDIR. 280–287.Google Scholar
- Eytan Bakshy, Solomon Messing, and Lada A Adamic. 2015. Exposure to ideologically diverse news and opinion on Facebook. Science 348, 6239 (2015), 1130–1132.Google Scholar
- Pablo Barberá. 2020. Social Media, Echo Chambers, and Political Polarization. Social Media and Democracy: The State of the Field, Prospects for Reform (2020), 34.Google Scholar
- Pablo Barberá, John T Jost, Jonathan Nagler, Joshua A Tucker, and Richard Bonneau. 2015. Tweeting from left to right: Is online political communication more than an echo chamber?Psychological science 26, 10 (2015), 1531–1542.Google ScholarCross Ref
- Fabian Baumann, Philipp Lorenz-Spreen, Igor M Sokolov, and Michele Starnini. 2020. Modeling echo chambers and polarization dynamics in social networks. Physical Review Letters 124, 4 (2020), 048301. Publisher: APS.Google ScholarCross Ref
- Antoine Bordes, Sumit Chopra, and Jason Weston. 2014. Question answering with subgraph embeddings. arXiv preprint arXiv:1406.3676(2014).Google Scholar
- Antoine Bordes, Nicolas Usunier, Alberto Garcia-Duran, Jason Weston, and Oksana Yakhnenko. 2013. Translating embeddings for modeling multi-relational data. In Neural Information Processing Systems (NIPS). 1–9.Google Scholar
- Jennifer Brundidge. 2010. Encountering “difference” in the contemporary public sphere: The contribution of the Internet to the heterogeneity of political discussion networks. Journal of Communication 60, 4 (2010), 680–700. Publisher: Oxford University Press.Google ScholarCross Ref
- Axel Bruns. 2017. Echo chamber? What echo chamber? Reviewing the evidence. In 6th Biennial Future of Journalism Conference (FOJ17).Google Scholar
- Axel Bruns. 2019. It’s not the technology, stupid: How the ‘Echo Chamber’and ‘Filter Bubble’metaphors have failed us. International Association for Media and Communication Research (2019).Google Scholar
- Matteo Cinelli, Gianmarco De Francisci Morales, Alessandro Galeazzi, Walter Quattrociocchi, and Michele Starnini. 2020. Echo chambers on social media: A comparative analysis. arXiv preprint arXiv:2004.09603(2020).Google Scholar
- Yuanfei Dai, Shiping Wang, Neal N Xiong, and Wenzhong Guo. 2020. A Survey on Knowledge Graph Embedding: Approaches, Applications and Benchmarks. Electronics 9, 5 (2020), 750.Google Scholar
- Pranav Dandekar, Ashish Goel, and David T Lee. 2013. Biased assimilation, homophily, and the dynamics of polarization. Proceedings of the National Academy of Sciences 110, 15(2013), 5791–5796.Google ScholarCross Ref
- Rajarshi Das, Arvind Neelakantan, David Belanger, and Andrew McCallum. 2016. Chains of reasoning over entities, relations, and text using recurrent neural networks. arXiv preprint arXiv:1607.01426(2016).Google Scholar
- Pasquale De Meo, Emilio Ferrara, Giacomo Fiumara, and Alessandro Provetti. 2011. Generalized louvain method for community detection in large networks. In 2011 11th international conference on intelligent systems design and applications. IEEE, 88–93.Google ScholarCross Ref
- Pasquale De Meo, Emilio Ferrara, Giacomo Fiumara, and Alessandro Provetti. 2014. On Facebook, most ties are weak. Commun. ACM 57, 11 (2014), 78–84. Publisher: ACM New York, NY, USA.Google ScholarDigital Library
- Morris H DeGroot. 1974. Reaching a consensus. J. Amer. Statist. Assoc. 69, 345 (1974), 118–121.Google ScholarCross Ref
- Michela Del Vicario, Antonio Scala, Guido Caldarelli, H Eugene Stanley, and Walter Quattrociocchi. 2017. Modeling confirmation bias and polarization. Scientific reports 7(2017), 40391.Google Scholar
- Elizabeth Dubois and Grant Blank. 2018. The echo chamber is overstated: the moderating effect of political interest and diverse media. Information, communication & society 21, 5 (2018), 729–745.Google Scholar
- Leon Festinger. 1962. A theory of cognitive dissonance. Vol. 2. Stanford university press.Google Scholar
- Dieter Frey. 1986. Recent research on selective exposure to information. Advances in experimental social psychology 19 (1986), 41–80. Publisher: Elsevier.Google Scholar
- Noah E Friedkin. 2006. A structural theory of social influence. Number 13. Cambridge University Press.Google Scholar
- Kiran Garimella, Gianmarco De Francisci Morales, Aristides Gionis, and Michael Mathioudakis. 2018. Political discourse on social media: Echo chambers, gatekeepers, and the price of bipartisanship. In Proceedings of the 2018 World Wide Web Conference. 913–922.Google ScholarDigital Library
- R Kelly Garrett. 2009. Echo chambers online?: Politically motivated selective exposure among Internet news users. Journal of computer-mediated communication 14, 2 (2009), 265–285. Publisher: Oxford University Press Oxford, UK.Google ScholarCross Ref
- Stefan Geiß, Melanie Magin, Pascal Jürgens, and Birgit Stark. 2021. Loopholes in the Echo Chambers: How the Echo Chamber Metaphor Oversimplifies the Effects of Information Gateways on Opinion Expression. Digital Journalism (2021), 1–27. Publisher: Taylor & Francis.Google Scholar
- Daniel Geschke, Jan Lorenz, and Peter Holtz. 2019. The triple-filter bubble: Using agent-based modelling to test a meta-theoretical framework for the emergence of filter bubbles and echo chambers. British Journal of Social Psychology 58, 1 (2019), 129–149.Google ScholarCross Ref
- Salvatore Giorgi, Sharath Chandra Guntuku, Muhammad Rahman, McKenzie Himelein-Wachowiak, Amy Kwarteng, and Brenda Curtis. 2020. Twitter corpus of the# blacklivesmatter movement and counter protests: 2013 to 2020. arXiv preprint arXiv:2009.00596(2020).Google Scholar
- Eduardo Graells-Garrido, Mounia Lalmas, and Daniele Quercia. 2014. People of opposing views can share common interests. In Proceedings of the 23rd International Conference on World Wide Web. 281–282.Google ScholarDigital Library
- Catherine Grevet, Loren G Terveen, and Eric Gilbert. 2014. Managing political differences in social media. In Proceedings of the 17th ACM conference on Computer supported cooperative work & social computing. 1400–1408.Google ScholarDigital Library
- Qingyu Guo, Fuzhen Zhuang, Chuan Qin, Hengshu Zhu, Xing Xie, Hui Xiong, and Qing He. 2020. A Survey on Knowledge Graph-Based Recommender Systems. arXiv preprint arXiv:2003.00911(2020).Google Scholar
- Aric Hagberg, Pieter Swart, and Daniel S Chult. 2008. Exploring network structure, dynamics, and function using NetworkX. Technical Report. Los Alamos National Lab.(LANL), Los Alamos, NM (United States).Google Scholar
- Will Hamilton, Payal Bajaj, Marinka Zitnik, Dan Jurafsky, and Jure Leskovec. 2018. Embedding logical queries on knowledge graphs. In Advances in neural information processing systems. 2026–2037.Google Scholar
- Curtis P Haugtvedt and Duane T Wegener. 1994. Message order effects in persuasion: An attitude strength perspective. Journal of consumer research 21, 1 (1994), 205–218. Publisher: The University of Chicago Press.Google ScholarCross Ref
- Rainer Hegselmann, Ulrich Krause, and others. 2002. Opinion dynamics and bounded confidence models, analysis, and simulation. Journal of artificial societies and social simulation 5, 3 (2002).Google Scholar
- John E Hunter, Jeffrey E Danes, and Stanley H Cohen. 2014. Mathematical models of attitude change: Change in single attitudes and cognitive structure. Vol. 1. Academic Press.Google Scholar
- Kathleen Hall Jamieson and Joseph N Cappella. 2008. Echo chamber: Rush Limbaugh and the conservative media establishment. Oxford University Press.Google Scholar
- Ray Jiang, Silvia Chiappa, Tor Lattimore, András György, and Pushmeet Kohli. 2019. Degenerate feedback loops in recommender systems. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society. 383–390.Google ScholarDigital Library
- Thomas Kelly. 2005. The epistemic significance of disagreement. Oxford studies in epistemology 1, 167-196 (2005).Google Scholar
- Diederik P Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980(2014).Google Scholar
- Yehuda Koren, Robert Bell, and Chris Volinsky. 2009. Matrix factorization techniques for recommender systems. Computer 42, 8 (2009), 30–37. Publisher: IEEE.Google ScholarDigital Library
- Hoon Lee, Nojin Kwak, and Scott W Campbell. 2015. Hearing the other side revisited: The joint workings of cross-cutting discussion and strong tie homogeneity in facilitating deliberative and participatory democracy. Communication Research 42, 4 (2015), 569–596. Publisher: SAGE Publications Sage CA: Los Angeles, CA.Google ScholarCross Ref
- Charles M Macal and Michael J North. 2005. Tutorial on agent-based modeling and simulation. In Proceedings of the Winter Simulation Conference, 2005.IEEE, 14–pp.Google ScholarCross Ref
- Anuja Majmundar, Jon-Patrick Allem, Tess Boley Cruz, and Jennifer Beth Unger. 2018. The why we retweet scale. PloS one 13, 10 (2018), e0206076. Publisher: Public Library of Science San Francisco, CA USA.Google ScholarCross Ref
- Miller McPherson, Lynn Smith-Lovin, and James M Cook. 2001. Birds of a feather: Homophily in social networks. Annual review of sociology 27, 1 (2001), 415–444. Publisher: Annual Reviews 4139 El Camino Way, PO Box 10139, Palo Alto, CA 94303-0139, USA.Google Scholar
- Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013. Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781(2013).Google Scholar
- Diana C Mutz. 2006. Hearing the other side: Deliberative versus participatory democracy. Cambridge University Press.Google Scholar
- David G Myers and Helmut Lamm. 1976. The group polarization phenomenon.Psychological bulletin 83, 4 (1976), 602. Publisher: American Psychological Association.Google Scholar
- Mark EJ Newman. 2006. Modularity and community structure in networks. Proceedings of the national academy of sciences 103, 23(2006), 8577–8582. Publisher: National Acad Sciences.Google ScholarCross Ref
- C Thi Nguyen. 2020. Echo chambers and epistemic bubbles. (2020).Google Scholar
- Brendan Nyhan and Jason Reifler. 2010. When corrections fail: The persistence of political misperceptions. Political Behavior 32, 2 (2010), 303–330. Publisher: Springer.Google ScholarCross Ref
- Enrico Palumbo, Diego Monti, Giuseppe Rizzo, Raphaël Troncy, and Elena Baralis. 2020. entity2rec: Property-specific knowledge graph embeddings for item recommendation. Expert Systems with Applications 151 (2020), 113235. Publisher: Elsevier.Google ScholarCross Ref
- Enrico Palumbo, Giuseppe Rizzo, Raphaël Troncy, Elena Baralis, Michele Osella, and Enrico Ferro. 2018. Knowledge graph embeddings with node2vec for item recommendation. In European Semantic Web Conference. Springer, 117–120.Google ScholarCross Ref
- Eli Pariser. 2011. The filter bubble: What the Internet is hiding from you. Penguin UK.Google ScholarDigital Library
- Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, and others. 2019. Pytorch: An imperative style, high-performance deep learning library. arXiv preprint arXiv:1912.01703(2019).Google Scholar
- Hongyu Ren, Weihua Hu, and Jure Leskovec. 2020. Query2box: Reasoning over knowledge graphs in vector space using box embeddings. arXiv preprint arXiv:2002.05969(2020).Google Scholar
- Kazutoshi Sasahara, Wen Chen, Hao Peng, Giovanni Luca Ciampaglia, Alessandro Flammini, and Filippo Menczer. 2020. Social influence and unfollowing accelerate the emergence of echo chambers. Journal of Computational Social Science(2020), 1–22. Publisher: Springer.Google Scholar
- Jaime E Settle. 2018. Frenemies: How social media polarizes America. Cambridge University Press.Google ScholarCross Ref
- Muzafer Sherif and Carl I Hovland. 1961. Social judgment: Assimilation and contrast effects in communication and attitude change.(1961). Publisher: Yale Univer. Press.Google Scholar
- Cass R Sunstein. 2001. Republic. com. Princeton university press.Google Scholar
- Cass R Sunstein. 2018. # Republic: Divided democracy in the age of social media. Princeton University Press.Google Scholar
- Henri Tajfel, John C Turner, William G Austin, and Stephen Worchel. 1979. An integrative theory of intergroup conflict. Organizational identity: A reader 56, 65 (1979), 9780203505984–16.Google Scholar
- Joshua A Tucker, Andrew Guess, Pablo Barberá, Cristian Vaccari, Alexandra Siegel, Sergey Sanovich, Denis Stukal, and Brendan Nyhan. 2018. Social media, political polarization, and political disinformation: A review of the scientific literature. Political polarization, and political disinformation: a review of the scientific literature (March 19, 2018)(2018).Google Scholar
- Xin Wang, Antonio D Sirianni, Shaoting Tang, Zhiming Zheng, and Feng Fu. 2020. Public discourse and social network echo chambers driven by socio-cognitive biases. Physical Review X 10, 4 (2020), 041042. Publisher: APS.Google Scholar
- Zhen Wang, Jianwen Zhang, Jianlin Feng, and Zheng Chen. 2014. Knowledge graph embedding by translating on hyperplanes. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 28. Issue: 1.Google ScholarCross Ref
- David Westerman, Patric R Spence, and Brandon Van Der Heide. 2014. Social media as information source: Recency of updates and credibility of information. Journal of computer-mediated communication 19, 2 (2014), 171–183. Publisher: Oxford University Press Oxford, UK.Google ScholarDigital Library
- John Woods. 2005. Epistemic Bubbles.. In We Will Show Them!(2). 731–774.Google Scholar
- Ruobing Xie, Zhiyuan Liu, Jia Jia, Huanbo Luan, and Maosong Sun. 2016. Representation learning of knowledge graphs with entity descriptions. In Thirtieth AAAI Conference on Artificial Intelligence.Google ScholarDigital Library
- Moran Yarchi, Christian Baden, and Neta Kligler-Vilenchik. 2020. Political polarization on the digital sphere: A cross-platform, over-time analysis of interactional, positional, and affective polarization on social media. Political Communication(2020), 1–42.Google Scholar
- Fuzheng Zhang, Nicholas Jing Yuan, Defu Lian, Xing Xie, and Wei-Ying Ma. 2016. Collaborative knowledge base embedding for recommender systems. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining. 353–362.Google ScholarDigital Library
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