Political Discourse on Social Media: Echo Chambers, Gatekeepers, and the Price of Bipartisanship
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Echo chambers, i.e., situations where one is exposed only to opinions that agree with their own, are an increasing concern for the political discourse in many democratic countries. This paper studies the phenomenon of political echo chambers on social media. We identify the two components in the phenomenon: the opinion that is shared, and the »chamber» (i.e., the social network) that allows the opinion to »echo» (i.e., be re-shared in the network) -- and examine closely at how these two components interact. We define a production and consumption measure for social-media users, which captures the political leaning of the content shared and received by them. By comparing the two, we find that Twitter users are, to a large degree, exposed to political opinions that agree with their own. We also find that users who try to bridge the echo chambers, by sharing content with diverse leaning, have to pay a »price of bipartisanship» in terms of their network centrality and content appreciation. In addition, we study the role of »gatekeepers,» users who consume content with diverse leaning but produce partisan content (with a single-sided leaning), in the formation of echo chambers. Finally, we apply these findings to the task of predicting partisans and gatekeepers from social and content features. While partisan users turn out relatively easy to identify, gatekeepers prove to be more challenging.
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Users show a tendency to favor information adhering to their beliefs and join groups formed around a shared narrative, that is, echo chambers.
“Furthermore, users show a tendency to favor information adhering to their beliefs and join groups formed around a shared narrative, that is, echo chambers (1, 14–18).”
From The echo chamber effect on social media (2021) · cites this paper as [16]
Cites
1 retracted
Limited individual attention and online virality of low-quality information
Qiu, X., Oliveira, D. F. M., Shirazi, A. S., Flammini, A., & Menczer, F. (2017) · Nature Human Behaviour · doi:10.1038/s41562-017-0132 · Cited by 186 · see citing works
RetractedBirds of a Feather: Homophily in Social Networks
McPherson, M., Smith-Lovin, L., & Cook, J. M. (2001) · Annual Review of Sociology · doi:10.1146/annurev.soc.27.1.415 · Cited by 18,567 · see citing works
(1999) · Cited by 12,854 · see citing works
Lord, C. G., Ross, L., & Lepper, M. R. (1979) · Journal of Personality and Social Psychology · doi:10.1037/0022-3514.37.11.2098 · Cited by 4,583 · see citing works
THE GENERALIZATION OF ‘STUDENT'S’ PROBLEM WHEN SEVERAL DIFFERENT POPULATION VARLANCES ARE INVOLVED
WELCH, B. L. (1947) · Biometrika · doi:10.1093/biomet/34.1-2.28 · Cited by 4,135 · see citing works
Exposure to ideologically diverse news and opinion on Facebook
Bakshy, E., Messing, S., & Adamic, L. A. (2015) · Science · doi:10.1126/science.aaa1160 · Cited by 3,245 · see citing works
The political blogosphere and the 2004 U.S. election: divided they blog
Adamic, L. A., & Glance, N. (2005) · Proceedings of the 3rd international workshop on Link discovery · doi:10.1145/1134271.1134277 · Cited by 2,619 · see citing works
The Generalization of `Student's' Problem when Several Different Population Variances are Involved
Welch, B. L. (1947) · Biometrika · doi:10.2307/2332510 · Cited by 1,946 · see citing works
The Fox News Effect: Media Bias and Voting
DellaVigna, S., & Kaplan, E. (2007) · The Quarterly Journal of Economics · doi:10.1162/qjec.122.3.1187 · Cited by 1,602 · see citing works
Tweeting From Left to Right: Is Online Political Communication More Than an Echo Chamber?
Barberá, P., Jost, J. T., Nagler, J., Tucker, J. A., & Bonneau, R. (2015) · Psychological Science · doi:10.1177/0956797615594620 · Cited by 1,472 · see citing works
Media and Political Polarization
Prior, M. (2013) · Annual Review of Political Science · doi:10.1146/annurev-polisci-100711-135242 · Cited by 1,125 · see citing works
Echo chambers online?: Politically motivated selective exposure among Internet news users
Garrett, R. K. (2009) · Journal of Computer-Mediated Communication · doi:10.1111/j.1083-6101.2009.01440.x · Cited by 1,040 · see citing works
Resurgent Mass Partisanship: The Role of Elite Polarization
Hetherington, M. J. (2001) · American Political Science Review · doi:10.1017/s0003055401003045 · Cited by 1,018 · see citing works
Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data
Barberá, P. (2015) · Political Analysis · doi:10.1093/pan/mpu011 · Cited by 982 · cited 6× · see citing works
“We adopt the latent space model proposed by Barberá et al. [7] to estimate a user polarity score.”
BackgroundResultInfluentialRecent Research on Selective Exposure to Information
Frey, D. (1986) · Advances in experimental social psychology · doi:10.1016/s0065-2601(08)60212-9 · Cited by 820 · see citing works
Bias in algorithmic filtering and personalization
Bozdag, E. (2013) · Ethics and Information Technology · doi:10.1007/s10676-013-9321-6 · Cited by 649 · see citing works
Sunstein, C. R. (2007) · Medical Entomology and Zoology · Cited by 621 · see citing works
DellaPosta, D., Shi, Y., & Macy, M. (2015) · American Journal of Sociology · doi:10.1086/681254 · Cited by 440 · see citing works
Quattrociocchi, W., Scala, A., & Sunstein, C. R. (2016) · SSRN Electronic Journal · doi:10.2139/ssrn.2795110 · Cited by 389 · see citing works
Lawrence, E., Sides, J., & Farrell, H. (2010) · Perspectives on Politics · doi:10.1017/s1537592709992714 · Cited by 352 · see citing works
Media Bias by the Numbers: Challenges and Opportunities in the Empirical Study of Partisan News
Groeling, T. J. (2013) · Annual Review of Political Science · doi:10.1146/annurev-polisci-040811-115123 · Cited by 213 · see citing works
Gatekeeping: A critical review
Barzilai‐Nahon, K. (2009) · Annual Review of Information Science and Technology · doi:10.1002/aris.2009.1440430117 · Cited by 208 · see citing works
A Long-Term Analysis of Polarization on Twitter
Garimella, V. R. K., & Weber, I. G. (2017) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v11i1.14918 · Cited by 189 · see citing works
Reducing Controversy by Connecting Opposing Views
Garimella, K., De Francisci Morales, G., Gionis, A., & Mathioudakis, M. (2017) · Proceedings of the Tenth ACM International Conference on Web Search and Data Mining · doi:10.1145/3018661.3018703 · Cited by 165 · see citing works
Self-citationMeasuring and moderating opinion polarization in social networks
Matakos, A., Terzi, E., & Tsaparas, P. (2017) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-017-0527-9 · Cited by 164 · see citing works
Quantifying Controversy in Social Media
Garimella, K., De Francisci Morales, G., Gionis, A., & Mathioudakis, M. (2016) · Proceedings of the Ninth ACM International Conference on Web Search and Data Mining · doi:10.1145/2835776.2835792 · Cited by 147 · cited 2× · see citing works
“For example, in the context of Twitter, clusters in retweet networks have been found to correlate with the political ideologies of Twitter users [7, 10, 15].”
BackgroundSelf-citationBlogs are Echo Chambers: Blogs are Echo Chambers
(2009) · 2009 42nd Hawaii International Conference on System Sciences · doi:10.1109/hicss.2009.91 · Cited by 121 · see citing works
Opinion polarization by learning from social feedback
Banisch, S., & Olbrich, E. (2019) · MPG.PuRe (Max Planck Society) · Cited by 107 · see citing works
A Long-Term Analysis of Polarization on TwitterPreprint
Garimella, K. V., & Weber, I. G. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1703.02769 · Cited by 76 · see citing works
Self-citationSharing political news: the balancing act of intimacy and socialization in selective exposure
An, J., Quercia, D., Cha, M., Gummadi, K. P., & Crowcroft, J. (2014) · EPJ Data Science · doi:10.1140/epjds/s13688-014-0012-2 · Cited by 75 · see citing works
An, J., Quercia, D., & Crowcroft, J. (2014) · Conference on Online Social Networks · doi:10.1145/2660460.2660469 · Cited by 71 · see citing works
Xu, W. W., & Feng, M. (2014) · Journal of Broadcasting & Electronic Media · doi:10.1080/08838151.2014.935853 · Cited by 67 · see citing works
‘Echo Chambers’: Partisan Facebook Groups during the 2014 Thai Election
Grömping, M. (2014) · Asia Pacific Media Educator · doi:10.1177/1326365x14539185 · Cited by 61 · see citing works
Audience Gatekeeping in the Twitter Service: An Investigation of Tweets about the 2009 Gaza Conflict
Kwon, K. H., Oh, O., Agrawal, M., Florida, U. O. S., Rao, H. R., & Buffalo, S. (2012) · AIS Transactions on Human-Computer Interaction · doi:10.17705/1thci.00047 · Cited by 58 · see citing works
The Effect of Collective Attention on Controversial Debates on Social Media
Garimella, K., De Francisci Morales, G., Gionis, A., & Mathioudakis, M. (2017) · Proceedings of the 2017 ACM on Web Science Conference · doi:10.1145/3091478.3091486 · Cited by 56 · cited 1× · see citing works
“5 The datasets are obtained by Garimella and Weber [19] and have already been validated in previous work [16].”
MethodSelf-citationBalancing Information Exposure in Social NetworksPreprint
Garimella, K., Gionis, A., Parotsidis, N., & Tatti, N. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1709.01491 · Cited by 56 · see citing works
Self-citationLu, H., Caverlee, J., & Niu, W. (2015) · doi:10.1145/2806416.2806573 · Cited by 45 · see citing works
Edwards, A. (2013) · Journal of Argumentation in Context · doi:10.1075/jaic.2.1.06edw · Cited by 36 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
The echo chamber effect on social media
Cinelli, M., De Francisci Morales, G., Galeazzi, A., Quattrociocchi, W., & Starnini, M. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2023301118 · Cited by 2,092 · see citing works
“For Twitter, we used tweets regarding three topics collected by Garimella et al. (16), namely Gun control, Obamacare, and abortion.”
MethodSelf-citationFeng, S., Park, C. Y., Liu, Y., & Tsvetkov, Y. (2023) · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2023.acl-long.656 · Cited by 149 · see citing works
Quantifying echo chamber effects in information spreading over political communication networks
Cota, W., Ferreira, S. C., Pastor-Satorras, R., & Starnini, M. (2019) · EPJ Data Science · doi:10.1140/epjds/s13688-019-0213-9 · Cited by 146 · see citing works
Falling into the Echo Chamber: The Italian Vaccination Debate on Twitter
Cossard, A., De Francisci Morales, G., Kalimeri, K., Mejova, Y., Paolotti, D., & Starnini, M. (2020) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v14i1.7285 · Cited by 126 · see citing works
Self-citationNo echo in the chambers of political interactions on Reddit
De Francisci Morales, G., Monti, C., & Starnini, M. (2021) · Scientific Reports · doi:10.1038/s41598-021-81531-x · Cited by 98 · see citing works
“Echo chambers are situations in which users have their beliefs reinforced due to repeated interactions with like-minded peers and insulation from others’ points of view [13, 14].”
BackgroundQuantifying ideological polarization on a network using generalized Euclidean distance
Hohmann, M., Devriendt, K., & Coscia, M. (2023) · Science Advances · doi:10.1126/sciadv.abq2044 · Cited by 74 · see citing works
Using sentiment analysis to predict opinion inversion in Tweets of political communication
Matalon, Y., Magdaci, O., Almozlino, A., & Yamin, D. (2021) · Scientific Reports · doi:10.1038/s41598-021-86510-w · Cited by 64 · see citing works
Epistemic Communities under Active Inference
Albarracin, M., Demekas, D., Ramstead, M. J. D., & Heins, C. (2022) · Entropy · doi:10.3390/e24040476 · Cited by 48 · see citing works
“, information provided by members of the outgroup) [42,48].”
BackgroundThrough the Newsfeed Glass: Rethinking Filter Bubbles and Echo Chambers
Figà Talamanca, G., & Arfini, S. (2022) · Philosophy & Technology · doi:10.1007/s13347-021-00494-z · Cited by 41 · see citing works
Affective Polarization in Online Climate Change Discourse on Twitter
Tyagi, A., Uyheng, J., & Carley, K. M. (2020) · 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) · doi:10.1109/asonam49781.2020.9381419 · Cited by 41 · see citing works
“a node belonging to the same or a different stance group [2], [28], [29].”
Background
- Authors
- Garimella, K., De Francisci Morales, G., Gionis, A., & Mathioudakis, M.
- Affiliations
- Aalto University (FI)
- Qatar Computing Research Institute, Doha, Qatar
- University of Helsinki (FI)
- Venue
- Proceedings of the 2018 World Wide Web Conference on World Wide Web - WWW '18
- Publisher
- ACM Press
- Published
- 2018
- Pages
- 913-922
- OpenAlex
- W2962972034
- Cited by
- 335
- References
- 38
- Citations by year
- 2026: 33 · 2025: 43 · 2024: 39 · 2023: 48 · 2022: 52 · 2021: 50 · 2020: 38 · 2019: 24 · 2018: 8
- FWCI
- 115.82
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- International World Wide Web Conference (WWW) (publishedVersion, cc-by)
- Työväentutkimus Vuosikirja (submittedVersion)
- Funders
- Academy of Finland (313927, 317085, 286211)
- European Commission (654024)
- Topics
- Social Sciences › Communication › Social Media and Politics
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Social Sciences › Sociology and Political Science › Media Influence and Politics
- Keywords
- echo chambers, political discourse, social media, Twitter, political polarization, gatekeepers, network centrality, content consumption, content creation
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