Me, My Echo Chamber, and I: Introspection on Social Media Polarization

Gillani, Nabeel; Yuan, Ann; Saveski, Martin; Vosoughi, Soroush; Roy, Deb

doi:10.1145/3178876.3186130

Cited by 118Social Media and PoliticsMisinformation and Its ImpactsMedia Influence and Politics

Me, My Echo Chamber, and I: Introspection on Social Media Polarization

Open access

Gillani, Nabeel, Yuan, Ann, Saveski, Martin, Vosoughi, Soroush, Roy, Deb·2018·Proceedings of the 2018 World Wide Web Conference on World Wide Web - WWW '18

DOI 10.1145/3178876.3186130

Open this paper in CoChat — free account Open PDF

Abstract

Homophily - our tendency to surround ourselves with others who share our perspectives and opinions about the world - is both a part of human nature and an organizing principle underpinning many of our digital social networks. However, when it comes to politics or culture, homophily can amplify tribal mindsets and produce "echo chambers" that degrade the quality, safety, and diversity of discourse online. While several studies have empirically proven this point, few have explored how making users aware of the extent and nature of their political echo chambers influences their subsequent beliefs and actions. In this paper, we introduce Social Mirror, a social network visualization tool that enables a sample of Twitter users to explore the politically-active parts of their social network. We use Social Mirror to recruit Twitter users with a prior history of political discourse to a randomized experiment where we evaluate the effects of different treatments on participants' i) beliefs about their network connections, ii) the political diversity of who they choose to follow, and iii) the political alignment of the URLs they choose to share. While we see no effects on average political alignment of shared URLs, we find that recommending accounts of the opposite political ideology to follow reduces participants» beliefs in the political homogeneity of their network connections but still enhances their connection diversity one week after treatment. Conversely, participants who enhance their belief in the political homogeneity of their Twitter connections have less diverse network connections 2-3 weeks after treatment. We explore the implications of these disconnects between beliefs and actions on future efforts to promote healthier exchanges in our digital public spheres.

Integrity signals

Registry

Crossref · 10.1145/3178876.3186130

Cites

29 of 29 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Combating social media's echo chamber effect through active exposure to diverse perspectives and networks represents a viable individual-level strategy for addressing the deepfake problem.
“Thus, combating social media's echo chamber effect through an active exposure to diverse perspectives and networks also represents a viable individual-level strategy for addressing the deepfake problem (Cinelli et al., 2021; Gillani et al., 2018).”
From Deepfakes: Deceptions, mitigations, and opportunities (2023) · cites this paper as Gillani et al., 2018

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
Affect, Not Ideology
Iyengar, S., Sood, G., & Lelkes, Y. (2012) · Public Opinion Quarterly · doi:10.1093/poq/nfs038 · Cited by 2,748 · 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
Political Polarization in the American Public
Fiorina, M. P., & Abrams, S. J. (2008) · Annual Review of Political Science · doi:10.1146/annurev.polisci.11.053106.153836 · Cited by 1,798 · see citing works
The Righteous Mind: Why Good People Are Divided by Politics and Religion
LaFollette, H., & Woodruff, M. L. (2013) · Philosophical Psychology · doi:10.1080/09515089.2013.838752 · Cited by 1,386 · see citing works
Field Experiments: Design, Analysis, and Interpretation
GREEN, D. P. (2024) · doi:10.61700/sgeirzx0v0mjh1252 · Cited by 1,381 · see citing works
The law of group polarization.
Sunstein, C. R. (2002) · Journal of Political Philosophy · doi:10.1111/1467-9760.00148 · Cited by 1,371 · see citing works
Network Diversity and Economic Development
Eagle, N., Macy, M. W., & Claxton, R. (2010) · Science · doi:10.1126/science.1186605 · Cited by 778 · see citing works
Understanding the Demographics of Twitter Users
Mislove, A. E., Lehmann, S., Ahn, Y., Onnela, J.-P. “., & Rosenquist, J. (2021) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v5i1.14168 · Cited by 761 · see citing works
Political Polarization on Twitter
Conover, M., Ratkiewicz, J., Francisco, M., Goncalves, B., Menczer, F., & Flammini, A. (2021) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v5i1.14126 · Cited by 626 · cited 2× · see citing works
“ed mention networks — suggesting homophilous endorsement patterns accompanied by a tendency for politically active users to inject partisan content and “call out” their counterparts on the other side [7]. Therefore, even when social media platforms create opportunities for cross-cutting dialog, the dialog is often muc…”
BackgroundInfluential
Dynamic debates: an analysis of group polarization over time on Twitter
Yardi, S., & Boyd, D. (2010) · Bulletin of Science, Technology & Society · doi:10.1177/0270467610380011 · Cited by 625 · see citing works
The Hostile Audience: The Effect of Access to Broadband Internet on Partisan Affect
Lelkes, Y., Sood, G., & Iyengar, S. (2017) · American Journal of Political Science · doi:10.1111/ajps.12237 · Cited by 476 · see citing works
Political Extremism Is Supported by an Illusion of Understanding
Fernbach, P. M., Rogers, T., Fox, C. R., & Sloman, S. A. (2013) · Psychological Science · doi:10.1177/0956797612464058 · Cited by 396 · see citing works
Tweetment Effects on the Tweeted: Experimentally Reducing Racist Harassment
Munger, K. (2017) · Political Behavior · doi:10.1007/s11109-016-9373-5 · Cited by 395 · see citing works
The Law of Group Polarization
Sunstein, C. R. (2003) · doi:10.1002/9780470690734.ch4 · Cited by 359 · see citing works
The tie that divides: Cross‐national evidence of the primacy of partyism
Westwood, S. J., Iyengar, S., Walgrave, S., Leonisio, R., Miller, L., & Strijbis, O. (2017) · European Journal of Political Research · doi:10.1111/1475-6765.12228 · Cited by 347 · see citing works
Is the Internet Causing Political Polarization? Evidence from Demographics
Boxell, L., Gentzkow, M., & Shapiro, J. (2017) · doi:10.3386/w23258 · Cited by 226 · cited 1× · see citing works
“America is still up for debate [6, 17], it appears that on platforms like Twitter, users are increasingly following accounts that share their own political views [12].”
Background
Assessing the relevance of node features for network structure
Ginestra Bianconi, A., Pin, P., & Matteo Marsili, A. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0811511106 · Cited by 222 · see citing works
Measuring online social bubbles
Nikolov, D., Oliveira, D. F. M., Flammini, A., & Menczer, F. (2015) · PeerJ Computer Science · doi:10.7717/peerj-cs.38 · Cited by 205 · 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
Designing and deploying online field experiments
Bakshy, E., Eckles, D., & Bernstein, M. S. (2014) · International World Wide Web Conference (WWW) · doi:10.1145/2566486.2567967 · Cited by 181 · 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
Encouraging Reading of Diverse Political Viewpoints with a Browser Widget
Munson, S. A., Lee, S., & Resnick, P. (2021) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v7i1.14429 · Cited by 136 · see citing works
Political Extremism is Supported by an Illusion of Understanding
Fernbach, P. M., Rogers, T., Fox, C. R., & Sloman, S. A. (2012) · PsycEXTRA Dataset · doi:10.1037/e519682015-069 · Cited by 43 · see citing works
Twitter Demographic Classification Using Deep Multi-modal Multi-task Learning
Vijayaraghavan, P., Vosoughi, S., & Roy, D. K. (2017) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p17-2076 · Cited by 39 · see citing works
Self-citation
Reducing Controversy by Connecting Opposing Views
Garimella, K., Morales, G. D. F., Gionis, A., & Mathioudakis, M. (2018) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2018/731 · Cited by 39 · see citing works
A House Divided? Roll Calls, Polarization, and Policy Differences in the U.S. House, 1877–2011
Bateman, D., Clinton, J. D., & Lapinski, J. S. (2016) · American Journal of Political Science · doi:10.1111/ajps.12281 · Cited by 34 · see citing works
Automatic Detection and Categorization of Election-Related Tweets
Vijayaraghavan, P., Vosoughi, S., & Roy, D. (2021) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v10i1.14816 · Cited by 26 · see citing works
Self-citation
FollowBias
Matias, J. N., Szalavitz, S., & Zuckerman, E. (2017) · ACM Conference on Computer Supported Cooperative Work (CSCW) · doi:10.1145/2998181.2998287 · Cited by 22 · see citing works

Flagged first, then by citations

Sorted by citations

Combating Fake News: A Survey on Identification and Mitigation Techniques
Sharma, K., Qian, F., Jiang, H., Ruchansky, N., Zhang, M., & Liu, Y. (2019) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3305260 · Cited by 514 · see citing works
Deepfakes: Deceptions, mitigations, and opportunities
Mustak, M., Salminen, J., Mäntymäki, M., Rahman, A., & Dwivedi, Y. K. (2023) · Journal of Business Research · doi:10.1016/j.jbusres.2022.113368 · Cited by 209 · see citing works
A review and provocation: On polarization and platforms
Kreiss, D., & McGregor, S. C. (2024) · New Media & Society · doi:10.1177/14614448231161880 · Cited by 151 · see citing works
Twitter Fingers and Echo Chambers: Exploring Expressions and Experiences of Online Racism Using Twitter
Criss, S., Michaels, E. K., Solomon, K., Allen, A. M., & Nguyen, T. T. X. (2020) · Journal of Racial and Ethnic Health Disparities · doi:10.1007/s40615-020-00894-5 · Cited by 101 · see citing works
#Scamdemic, #Plandemic, or #Scaredemic: What Parler Social Media Platform Tells Us about COVID-19 Vaccine
Baines, A. F., Ittefaq, M., & Abwao, M. (2021) · Vaccines · doi:10.3390/vaccines9050421 · Cited by 97 · see citing works
Network polarization, filter bubbles, and echo chambers: an annotated review of measures and reduction methods
Interian, R., G. Marzo, R., Mendoza, I., & Ribeiro, C. C. (2023) · International Transactions in Operational Research · doi:10.1111/itor.13224 · Cited by 67 · see citing works
The Role of Instagram, Facebook, and YouTube Frequency of Use in University Students’ Digital Skills Components
Perifanou, M. A., Tzafilkou, K., & Economides, A. A. (2021) · Education Sciences · doi:10.3390/educsci11120766 · Cited by 74 · see citing works
Characterizing Online Public Discussions through Patterns of Participant Interactions
Zhang, J., Danescu-Niculescu-Mizil, C., Sauper, C., & Taylor, S. J. (2018) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3274467 · Cited by 47 · see citing works
Challenging social media threats using collective well-being-aware recommendation algorithms and an educational virtual companion
Ognibene, D., Wilkens, R., Taibi, D., Hernández‐Leo, D., Kruschwitz, U., Donabauer, G., Theophilou, E., Lomonaco, F., Buršić, S., Lobo, R. A., et al. (2023) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2022.654930 · Cited by 55 · see citing works
ChamberBreaker: Mitigating the Echo Chamber Effect and Supporting Information Hygiene through a Gamified Inoculation System
Jeon, Y., Kim, B., Xiong, A., Lee, D., & Han, K. (2021) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3479859 · Cited by 42 · see citing works
See Widely, Think Wisely: Toward Designing a Generative Multi-agent System to Burst Filter Bubbles
Zhang, Y., Sun, J., Feng, L., Yao, C., Fan, M., Zhang, L., Wang, Q., Geng, X., & Rui, Y. (2024) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642545 · Cited by 44 · see citing works
A Survey on Computational Politics
Haq, E.-U., Braud, T., Kwon, Y. D., & Hui, P. (2020) · IEEE Access · doi:10.1109/access.2020.3034983 · Cited by 40 · see citing works
Social Networking Sites and Youth Transition: The Use of Facebook and Personal Well-Being of Social Work Young Graduates
de Mesa, J. C., Jacinto, L. G., Peláez, A. L., & Erro‐Garcés, A. (2020) · Frontiers in Psychology · doi:10.3389/fpsyg.2020.00230 · Cited by 38 · see citing works
On the impossibility of breaking the echo chamber effect in social media using regulation
Avin, C., Daltrophe, H., & Lotker, Z. (2024) · Scientific Reports · doi:10.1038/s41598-023-50850-6 · Cited by 36 · see citing works
Designing a Clinician-Facing Tool for Using Insights From Patients’ Social Media Activity: Iterative Co-Design Approach
Yoo, D. W., Birnbaum, M., Meter, A. V., Ali, A. F., Arenare, E., Abowd, G., & de Choudhury, M. (2020) · JMIR Mental Health · doi:10.2196/16969 · Cited by 36 · see citing works
Learning in and about a filtered universe: young people’s awareness and control of algorithms in social media
de Groot, T., de Haan, M., & van Dijken, M. (2023) · Learning Media and Technology · doi:10.1080/17439884.2023.2253730 · Cited by 34 · see citing works
Disinformation detection on social media: An integrated approach
Rastogi, S., & Bansal, D. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-13129-y · Cited by 31 · see citing works
Opinion influence maximization problem in online social networks based on group polarization effect
Dai, J., Zhu, J., & Wang, G. (2022) · Information Sciences · doi:10.1016/j.ins.2022.07.086 · Cited by 30 · see citing works
The Driverless Cars Emulsion: Using participatory foresight and constructive conflict to address transport’s wicked problems
Lyons, G. D. (2021) · Futures · doi:10.1016/j.futures.2021.102889 · Cited by 28 · see citing works
Intergroup Contact in the Wild
Zhang, J. S., Tan, C., & Lv, Q. (2019) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3359295 · Cited by 27 · see citing works
POLE
Huang, Z., Silva, A., & Singh, A. K. (2022) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/3488560.3498454 · Cited by 27 · see citing works
Modularity and composite diversity affect the collective gathering of information online
Pescetelli, N., Rutherford, A., & Rahwan, I. (2021) · Nature Communications · doi:10.1038/s41467-021-23424-1 · Cited by 28 · see citing works
Adherence to Misinformation on Social Media Through Socio-Cognitive and Group-Based Processes
Efstratiou, A., & Cristofaro, E. D. (2022) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3555589 · Cited by 26 · see citing works
Evaluating Public Anxiety for Topic-Based Communities in Social Networks
Ta, N., Li, K., Yang, Y., Jiao, F., Tang, Z., & Li, G. (2020) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2020.2989759 · Cited by 24 · see citing works
Building relationships on social networking sites from a social work approach
de Mesa, J. C., Jacinto, L. G., Peláez, A. L., & García, M. D. L. O. P. (2019) · Journal of Social Work Practice · doi:10.1080/02650533.2019.1608429 · Cited by 13 · see citing works
Community-Based Recommendations on Twitter: Avoiding the Filter Bubble
Grossetti, Q., du Mouza, C., & Travers, N. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-34223-4_14 · Cited by 22 · see citing works
OtherTube: Facilitating Content Discovery and Reflection by Exchanging YouTube Recommendations with Strangers
Bhuiyan, M. M., Isaza, C. A. B., Mitra, T., & Lee, S. W. (2022) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3491102.3502028 · Cited by 22 · see citing works
Citizen Science on Twitter: Using Data Analytics to Understand Conversations and Networks
Suvodeep Mazumdar, A., & Thakker, D. (2020) · Future Internet · doi:10.3390/fi12120210 · Cited by 23 · see citing works
Fringe News Networks: Dynamics of US News Viewership following the 2020 Presidential Election
KhudaBukhsh, A. R., Sarkar, R., Kamlet, M. S., & Mitchell, T. M. (2022) · ACM Web Science Conference (WebSci) · doi:10.1145/3501247.3531577 · Cited by 23 · see citing works
Extending the Spiral of Silence: Theorizing a Typology of Political Self-Silencing
Chen, G. M., & Duchovnay, M. (2022) · Communication Studies · doi:10.1080/10510974.2022.2129401 · Cited by 21 · see citing works
Young People’s Digital Interactions from a Narrative Identity Perspective: Implications for Mental Health and Wellbeing
Granic, I., Morita, H., & Scholten, H. (2020) · Psychological Inquiry · doi:10.1080/1047840x.2020.1820225 · Cited by 23 · see citing works
Intercultural Computing Education: Toward Justice Across Difference
Arawjo, I., & Mogos, A. (2021) · ACM Transactions on Computing Education · doi:10.1145/3458037 · Cited by 20 · see citing works
After the Digital Tornado
Werbach, K., Marsden, C. T., Cohen, J. E., Wu, T., Hosanagar, K., Miller, A. P., Mayer-Schönberger, V., Kluttz, D. N., Kohli, N., Mulligan, D. K., et al. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108610018 · Cited by 19 · see citing works
Social Robots Outdo the Not-So-Social Media for Self-Disclosure: Safe Machines Preferred to Unsafe Humans?
Luo, R., Zhang, T. X. Y., Chen, D. H.-C., Hoorn, J. F., & Huang, I. S. (2022) · Robotics · doi:10.3390/robotics11050092 · Cited by 18 · see citing works
Birds of a Feather Get Angrier Together: Social Media News Use and Social Media Political Homophily as Antecedents of Political Anger
Cheng, Z., Marcos-Marné, H., & de Zúñiga, H. G. (2023) · Political Behavior · doi:10.1007/s11109-023-09864-z · Cited by 18 · see citing works
Proper network randomization is key to assessing social balance
Hao, B., & Kovács, I. A. (2024) · Science Advances · doi:10.1126/sciadv.adj0104 · Cited by 18 · see citing works
Semi-supervised Stance Detection of Tweets Via Distant Network Supervision
Dutta, S., Caur, S., Chakrabarti, S., & Chakraborty, T. (2022) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/3488560.3498511 · Cited by 17 · see citing works
Linear Opinion Dynamics Model With Higher Order Interactions
Xu, W.-Y., & Zhang, Z.-Z. (2023) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2023.3324144 · Cited by 15 · see citing works
Representativeness of Abortion Legislation Debate on Twitter: A Case Study in Argentina and Chile
Graells-Garrido, E., Baeza-Yates, R., & Lalmas, M. (2020) · The Web Conference · doi:10.1145/3366424.3383561 · Cited by 19 · see citing works
KHAN: Knowledge-Aware Hierarchical Attention Networks for Accurate Political Stance Prediction
Ko, Y., Ryu, S., Han, S., Jeon, Y., Kim, J., Park, S., Han, K., Tong, H., & Kim, S. (2023) · International World Wide Web Conference (WWW) · doi:10.1145/3543507.3583300 · Cited by 15 · see citing works
An Exploratory Study on Information Cocoon in Recommender Systems
Zhang, L., Lian, Y., Wu, H., Song, C., & Yuan, X. (2025) · Data Science and Engineering · doi:10.1007/s41019-025-00288-9 · Cited by 14 · see citing works
Echo Chambers and Algorithmic Bias: The Homogenization of Online Culture in a Smart Society
Putri, S. D. G., Purnomo, E. P., & Khairunnisa, T. (2024) · SHS Web of Conferences · doi:10.1051/shsconf/202420205001 · Cited by 20 · see citing works
Relationship between negative coping style and fear of COVID-19 among Wuhan college students during the post-pandemic period: A moderated chain mediation model
Yang, L., Yang, Z., & Xia, Y. (2022) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2022.994685 · Cited by 14 · see citing works
Homofilia, polarización afectiva y desinformación en Twitter. Caso de estudio sobre la crisis migratoria #Openarms
Castillo-de-Mesa, J., Méndez-Domínguez, P., Carbonero-Muñoz, D., & Gómez-Jacinto, L. (2021) · Redes. Revista hispana para el análisis de redes sociales · doi:10.5565/rev/redes.913 · Cited by 13 · see citing works
Digital competences and skills as key factors between connectedness and tolerance to diversity on social networking sites: Case study of social work graduates on Facebook
Castillo de Mesa, J., & Gómez Jacinto, L. (2022) · Current Sociology · doi:10.1177/0011392120983341 · Cited by 13 · see citing works
The Role of Health Belief Model Constructs and Content Creator Characteristics in Social Media Engagement: Insights from COVID-19 Vaccine Tweets
Jia, X., Ahn, S., Seelig, M. I., & Morgan, S. E. (2024) · Healthcare · doi:10.3390/healthcare12181845 · Cited by 12 · see citing works
The Coevolution of Social Networks and Cognitive Dissonance
Whitaker, R. M., Colombo, G. B., Turner, L. D., Dunham, Y. C., Doyle, D., Roy, E. M., & Giammanco, C. A. (2021) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2021.3090833 · Cited by 12 · see citing works
Biased Bots
Dingler, T., Choudhury, A., & Kostakos, V. (2018) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/3267305.3274189 · Cited by 12 · see citing works
Do Politicians Talk about Politics? Assessing Online Communication Patterns of Brazilian Politicians
Oliveira, L., de Melo, P. O. S. V., Amaral, M. S., & de Pinho, J. A. G. (2020) · ACM Transactions on Social Computing · doi:10.1145/3412326 · Cited by 11 · see citing works
"We're not all construction workers": Algorithmic Compression of Latinidad on TikTok
Lutz, N., & Aragon, C. (2024) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3687019 · Cited by 14 · see citing works
HearHere: Mitigating Echo Chambers in News Consumption through an AI-based Web System
Jeon, Y., Kim, J., Park, S., Ko, Y., Ryu, S., Kim, S., & Han, K. (2024) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3637340 · Cited by 9 · see citing works
Health Information-Seeking Behavior of Students at Kuwait University
Aldousari, E., & Al‐Muomen, N. (2020) · The International Information & Library Review · doi:10.1080/10572317.2020.1849913 · Cited by 9 · see citing works
BaitWatcher: A Lightweight Web Interface for the Detection of Incongruent News Headlines
Park, K., Kim, T., Yoon, S., Cha, M., & Jung, K. (2020) · Lecture notes in social networks · doi:10.1007/978-3-030-42699-6_12 · Cited by 9 · see citing works
Voting in the Echo Chamber? Patterns of Political Online Activities and Voting Behavior in Switzerland
Ackermann, K., & Stadelmann‐Steffen, I. (2022) · Swiss Political Science Review · doi:10.1111/spsr.12498 · Cited by 8 · see citing works
Caste Capital on Twitter: A Formal Network Analysis of Caste Relations among Indian Politicians
Vaghela, P., Mothilal, R. K., Romero, D. M., & Pal, J. (2022) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3512927 · Cited by 8 · see citing works
Social Work and Digital Activism: Sorority, Intersectionality, Homophily and Polarisation in #MeToo
de Mesa, J. C., Servós, C. M., Peláez, A. L., & Méndez-Domínguez, P. (2021) · Alternativas Cuadernos de trabajo Social · doi:10.14198/altern2021.28.2.09 · Cited by 8 · see citing works
Using impression data to improve models of online social influence
Liu, R., Greene, K. T., Liu, R., Mandic, M., Valentino, B. A., Vosoughi, S., & Subrahmanian, V. S. (2021) · Scientific Reports · doi:10.1038/s41598-021-96021-3 · Cited by 8 · see citing works
Self-citation
VoxPop: An Experimental Social Media Platform for Calibrated (Mis)information Discourse
Sharevski, F., Jachim, P., Pieroni, E., & Jachim, N. (2021) · New Security Paradigms Workshop · doi:10.1145/3498891.3498893 · Cited by 8 · see citing works
Measuring Network Polarization and Political Sectarianism During the 2020 Pandemic
Kamienski, C., Penteado, C. L. D. C., Goya, D., da Rocha, R. V., Mazim, L., di Gênova, D. V. B., Ramos, D. F. S., de França, F. O., Horita, F., & dos Santos, C. D. S. (2022) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2022.3159449 · Cited by 9 · see citing works
Comunicación digital de los colegios de trabajo social en España en las redes sociales online ¿coalición, jerarquización o aislamiento?
de Mesa, J. C., Peláez, A. L., Méndez-Domínguez, P., & Kirwan, G. (2022) · Revista Española de Investigaciones Sociológicas · doi:10.5477/cis/reis.178.39 · Cited by 7 · see citing works
How social media users adopt the toxic behaviors of ingroup and outgroup accounts
Zoizner, A., & Levy, A. (2025) · Journal of Computer-Mediated Communication · doi:10.1093/jcmc/zmaf018 · Cited by 7 · see citing works
Bridging Thoughts and Words: Graph-Based Intent-Semantic Joint Learning for Fake News Detection
Wang, Z., Sheng, Q., Wang, D., Hu, B., & Cao, J. (2025) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3746252.3761400 · Cited by 6 · see citing works
NewsGuesser: Using Curiosity to Reduce Selective Exposure
Sun, L., Zhang, H., Liu, E., Liu, M., & Vaccaro, K. (2024) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3637376 · Cited by 6 · see citing works
“Who can help me?”: Knowledge Infused Matching of Support Seekers and Support Providers during COVID-19 on Reddit
Gaur, M., Roy, K., Sharma, A., Srivastava, B., & Sheth, A. (2021) · IEEE International Conference on Healthcare Informatics (ICHI) · doi:10.1109/ichi52183.2021.00049 · Cited by 7 · see citing works
Effects of Incidental Brief Exposure to News on News Knowledge While Scrolling Through Videos
Takano, M., Ogawa, Y., Taka, F., & Morishita, S. (2021) · IEEE Access · doi:10.1109/access.2021.3063484 · Cited by 5 · see citing works
The role of social media messages and content creators in shaping COVID-19 vaccination intentions
Jia, X., Ahn, S., & Morgan, S. E. (2025) · Frontiers in Digital Health · doi:10.3389/fdgth.2025.1448884 · Cited by 5 · see citing works
Towards a Robust Framework for Multimodal Hate Detection: A Study on Video vs. Image-based Content
Koushik, G. A., Kanojia, D., & Treharne, H. (2025) · International World Wide Web Conference (WWW) · doi:10.1145/3701716.3718382 · Cited by 6 · see citing works
The Wisdom of the Crowd is not a Forgone Conclusion. Effects of Self-Selection on (Collaborative) Knowledge Construction
Krebs, M.-C., Oeberst, A., & von der Beck, I. (2023) · Topics in Cognitive Science · doi:10.1111/tops.12647 · Cited by 7 · see citing works
Socially Engineering a Polarizing Discourse on Facebook through Malware-Induced Misperception
Sharevski, F., Treebridge, P., Jachim, P., Li, A., Babin, A., & Westbrook, J. (2021) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2021.2009671 · Cited by 5 · see citing works
Echo Chambers within the Russo-Ukrainian War: The Role of Bipartisan Users
Zhang, P., Haq, E.-U., Zhu, Y., Hui, P., & Gareth Tyson, A. (2023) · IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) · doi:10.1145/3625007.3627475 · Cited by 5 · see citing works
A Framework for the Unsupervised Modeling and Extraction of Polarization Knowledge from News Media
Paschalides, D., Pallis, G., & Dikaiakos, M. D. (2024) · ACM Transactions on Social Computing · doi:10.1145/3703594 · Cited by 6 · see citing works
Revisiting contingency theory in regenerative social-mediated crisis: An investigation of Maxim’s and Yoshinoya in Hong Kong’s polarized context
Ao, S. H., Mak, A. K. Y., & Tsang, L. (2022) · Public Relations Review · doi:10.1016/j.pubrev.2022.102227 · Cited by 4 · see citing works
Search Support Tools
Umemoto, K., Yamamoto, T., & Tanaka, K. (2020) · Human-computer interaction series · doi:10.1007/978-3-030-38825-6_8 · Cited by 4 · see citing works
How violence among consumers alters the expected benefits of participation in a consumer collective
Dessart, L., Covà, B., & Davignon, N. (2024) · Journal of Consumer Affairs · doi:10.1111/joca.12594 · Cited by 3 · see citing works
Early morning hour and evening usage habits increase misinformation-spread
Stockinger, E., Gallotti, R., & Hausladen, C. I. (2024) · Scientific Reports · doi:10.1038/s41598-024-69447-8 · Cited by 4 · see citing works
Investigating Echo-Chambers in Decentralized Social Networks: A Mastodon Case Study
Huitema, I. R., Oskooei, A. R., Aktaş, M. S., & Riveni, M. (2025) · Studies in computational intelligence · doi:10.1007/978-3-031-82431-9_26 · Cited by 3 · see citing works
Intrinsic social media literacy: a public relations strategy addressing problematic social media use among teenagers globally
Makama, F. T., James, S. A., & Makama, E. F. (2025) · Cogent Social Sciences · doi:10.1080/23311886.2025.2518437 · Cited by 5 · see citing works
Social Influence Leads to the Formation of Diverse Local Trends
Epstein, Z. G., Groh, M., Dubey, A., & Pentland, A. (2021) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3479553 · Cited by 3 · see citing works
Influence and performance of user similarity metrics in followee prediction
Tommasel, A., & Godoy, D. L. (2020) · Journal of Information Science · doi:10.1177/0165551520975359 · Cited by 3 · see citing works
Superdiversity: New paths for social sciences in the upcoming future
Peláez, A. L., Álvarez-Pérez, P., & Harris, V. W. (2021) · Current Sociology · doi:10.1177/00113921211021934 · Cited by 3 · see citing works
The Rural Family Medicine Café Project: A Social Media Strategy to Reduce Occupational Isolation and Improve Support for Rural Healthcare Professionals
Wheatley, A., Floss, M., Bakola, M., Kampouraki, M., Silveira, B. N., & Scott‐Jones, J. (2020) · Frontiers in Public Health · doi:10.3389/fpubh.2020.595255 · Cited by 3 · see citing works
Semiosis in Artificial Intelligence-Mediated Environments: Exploring the Signs of Social Media Burnout
Arkhipova, D. (2024) · Theory and history in the human and social sciences · doi:10.1007/978-3-031-47001-1_11 · Cited by 2 · see citing works
Twitter polarity and computational propaganda: the case of the Woman, Life, Freedom movement in Iran
Rahbari, M. M., Blanchard, P., & Safari, H. (2025) · Journal of Information Technology & Politics · doi:10.1080/19331681.2025.2537270 · Cited by 2 · see citing works
Breaking the Loop: Causal Learning to Mitigate Echo Chambers in Social Networks
Yu, D., Li, Q., Huo, H., & Xu, G. (2025) · ACM Transactions on Information Systems · doi:10.1145/3757738 · Cited by 2 · see citing works
Polarisation: the new 'superword'. Meanings and current uses in English and Spanish
Requejo, M. D. P., & Romano, M. (2025) · Journal of Corpora and Discourse Studies · doi:10.18573/jcads.137 · Cited by 2 · see citing works
Linking the Dynamics of User Stance to the Structure of Online Discussions
Largeron, C., Mardale, A., & Rizoiu, M.-A. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-74251-5_22 · Cited by 2 · see citing works
Yes, Echo-Chambers Mislead You Too: A Game-Based Educational Experience to Reveal the Impact of Social Media Personalization Algorithms
Lomonaco, F., Taibi, D., Trianni, V., Buršić, S., Donabauer, G., & Ognibene, D. (2023) · Communications in computer and information science · doi:10.1007/978-3-031-29800-4_26 · Cited by 3 · see citing works
Generic or Specific Search Terms: What Do Citizens Type in the Google Search Bar to Obtain Political Information?
Zumofen, G. (2023) · Journal of Information Technology & Politics · doi:10.1080/19331681.2023.2221681 · Cited by 2 · see citing works
Understanding the Overlap of Online Offending and Victimization: Using Cluster Analysis to Examine Group Differences
Parti, K., Dearden, T. E., & E.Hawdon, J. (2023) · Routledge eBooks · doi:10.4324/9781003429678-7 · Cited by 1 · see citing works
The impact of algorithm awareness training on competent interaction with intelligent voice assistants
Markus, A., Baumann, M., Pfister, J., Andreas Hotho, A., Carolus, A., & Wienrich, C. (2025) · Discover Education · doi:10.1007/s44217-025-00522-6 · Cited by 2 · see citing works
Cognitive Ethical Design and Evaluation of Productive Reinforcing Spiral Model to Mitigate the Challenge of Extreme Polarization
Pimentel, A. P., da Motta, C. L. R., Correia, A., de Souza, J. M., & Schneider, D. (2025) · International Conference on Computer Supported Cooperative Work in Design (CSCWD) · doi:10.1109/cscwd64889.2025.11033464 · Cited by 1 · see citing works
A Novel Framework for Evaluating Polarization in Online Social Networks
Buratti, C., Marchetti, M., Parlapiano, F., Domenico Ursino, A., & Virgili, L. (2025) · Big Data and Cognitive Computing · doi:10.3390/bdcc9090227 · Cited by 1 · see citing works
FAIR-SE: Framework for Analyzing Information Disparities in Search Engines with Diverse LLM-Generated Personas
You, J., Hong, S.-K., Liu, L., Lee, K., & Kwon, H.-Y. (2025) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3746252.3761361 · Cited by 3 · see citing works
A Knowledge Graph-Based Framework for Integrated Network-Centric Warfare Strategies for Cyber-Physical-Social World
Malick, R. A. S., Murtaza, M., & Qureshi, K. A. (2022) · International Conference on Cyber Warfare and Security (ICCWS) · doi:10.1109/iccws56285.2022.9998467 · Cited by 1 · see citing works
How article category in Wikipedia determines the heterogeneity of its editors
Oeberst, A., & Ridderbecks, T. (2024) · Scientific Reports · doi:10.1038/s41598-023-50448-y · Cited by 3 · see citing works
Positive Communities on Signed Graphs That Are Not Echo Chambers: A Clique-Based Approach
Zhou, A., Wang, Y., Chen, L., & ÖZSU, M. T. (2024) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde60146.2024.00199 · Cited by 1 · see citing works
A Review of Group Polarization Research from a Dynamics Perspective
Fu, W., Zhu, R., Liu, S., Lu, X. B., & Li, B. (2025) · Journalism and Media · doi:10.3390/journalmedia6030144 · Cited by 2 · see citing works
Archives, fragments Web et diasporas : pour une exploration désagrégée de corpus d'archives Web liées aux représentations en ligne des diasporas
Lobbé, Q. (2018) · doi:10.70675/a5d74b3cz1af2z464bz90c0ze29ba9430b83 · Cited by 1 · see citing works
Concluding Remarks and Challenges
Tagarelli, A., & Interdonato, R. (2018) · SpringerBriefs in computer science · doi:10.1007/978-3-030-00229-9_9
DE-POLARIZING BY COLORING, REASONING BY CURATING
Goldberg, T., Wecker, A. J., Tabashi, T., & Reinhartz-Berger, J. L. A. I. (2022) · IADIS International Conference on e-Society (ES) · doi:10.33965/es_ml2022_202202c042

Me, My Echo Chamber, and I: Introspection on Social Media Polarization
Gillani, N., Yuan, A., Saveski, M., Vosoughi, S., & Roy, D. (2018) · arXiv · doi:10.48550/arxiv.1803.01731 · Cited by 33 · see citing works
Self-citation
Is There an Echo in Here? Is the Echo Damaging? The Causes and Effects of Political Homogeneity in Online Networks
Neubaum, G., Cargnino, M., Frauhammer, L. T., & Röchert, D. (2026) · Springer eBooks · doi:10.1007/978-3-658-49118-5_3
Pendulating or resonating? A case of echo-chambers in twitter
Gupta, V., Dass, P., & Arora, R. (2022) · Journal of Discrete Mathematical Sciences and Cryptography · doi:10.1080/09720529.2021.2019442 · Cited by 7 · see citing works
Political Discourse on Social Media: Echo Chambers, Gatekeepers, and the Price of Bipartisanship
Garimella, K., De Francisci Morales, G., Gionis, A., & Mathioudakis, M. (2018) · Proceedings of the 2018 World Wide Web Conference on World Wide Web - WWW '18 · doi:10.1145/3178876.3186139 · Cited by 335 · see citing works
Partisan Memes as a Catalyst for Homophilous Networks
Kim, Y., Collier, J. R., Murray, C., & Stroud, N. J. (2023) · Political Communication · doi:10.1080/10584609.2023.2222364 · Cited by 14 · see citing works

Authors: Gillani, N., Yuan, A., Saveski, M., Vosoughi, S., & Roy, D.
Affiliations: Massachusetts Institute of Technology (US)
ORCID: Gillani, Nabeel
Saveski, Martin
Vosoughi, Soroush
Roy, Deb
Venue: Proceedings of the 2018 World Wide Web Conference on World Wide Web - WWW '18
Publisher: ACM Press
Published: 2018
Pages: 823-831
DOI: 10.1145/3178876.3186130
OpenAlex: W4300918441
Cited by: 117
References: 29
Citations by year: 2026: 9 · 2025: 20 · 2024: 17 · 2023: 11 · 2022: 20 · 2021: 17 · 2020: 15 · 2019: 4 · 2018: 4
FWCI: 38.61
Type: conference paper
Language: en
Open access: hybrid
doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0/ (vor)
Locations: International World Wide Web Conference (WWW) (publishedVersion, cc-by)
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 homophily, social network visualization, Twitter, political polarization, network diversity, social mirror, online political discourse, public sphere

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Me, My Echo Chamber, and I: Introspection on Social Media Polarization

Abstract

Integrity signals

Claims the paper makes

Claims other papers make about this one

Cites

Cited by

Related

Record

About these checks