CoChat
Sign Up
Cited by 93Opinion Dynamics and Social InfluenceSocial Media and PoliticsMisinformation and Its Impacts

Public Discourse and Social Network Echo Chambers Driven by Socio-Cognitive Biases

Open access
Wang, Xin, Sirianni, Antonio D., Tang, Shaoting, Zheng, Zhiming, Fu, Feng·2020·Physical Review X
DOI 10.1103/physrevx.10.041042
Open this paper in CoChat — free accountOpen PDF

Abstract

In recent years, social media has become an important platform for political discourse, being a site of both political conversations between voters and political advertisements from campaigns.While their individual influences on public discourse are well documented, the interplay between individual-level cognitive biases, social influence processes, dueling campaign efforts, and social media platforms remains unexamined.We introduce an agent-based model that integrates these dynamics and illustrates how their combination can lead to the formation of echo chambers.We find that the range of political viewpoints that individuals are willing to consider is a key determinant in the formation of polarized networks and the emergence of echo chambers and show that aggressive political campaigns can have counterproductive outcomes by radicalizing supporters and alienating moderates.Our model results demonstrate how certain elements of public discourse and political polarization can be understood as the result of an interactive process of shifting individual opinions, evolving social networks, and political campaigns.We also introduce a dynamic empirical case, retweet networks from the final stage of the 2016 U.S. presidential election, to show how our proposed model can be calibrated with real-world behavior.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1103/physrevx.10.041042

Cites

8 of 8 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

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?

  • On Weibo, Wang et al. observed that retweeting mechanisms promote polarization, while commenting fosters consensus, highlighting the importance of interaction types in shaping polarization dynamics.

    “On Weibo, Wang et al. [78] observed that retweeting mechanisms promote polarization, while commenting fosters consensus, highlighting the importance of interaction types in shaping polarization dynamics.”

    From A systematic review of echo chamber research: comparative analysis of conceptualizations, operationalizations, and varying outcomes (2025) · cites this paper as [78]

    The Results and model–data integration sections describe increasingly polarized retweet networks on Twitter during the 2016 U.S. election. A figure caption in Appendix E says that discussion-partner influence can reduce extreme clusters in simulations. The paper does not report observations on Weibo or an empirical comparison showing that commenting fosters consensus. (LLM, conf=0.85)

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 52

  1. S. Plous, The Psychology of Judgment and Decision Making (McGraw-Hill, New York, 1993).
  2. R. S. Nickerson, Confirmation Bias: A Ubiquitous Phenomenon in Many Guises, Rev. Gen. Psychol. 2, 175 (1998). · DOI · Paper page
  3. E. Jonas, S. Schulz-Hardt, D. Frey, and N. Thelen, Confirmation Bias in Sequential Information Search after Preliminary Decisions: An Expansion of Dissonance Theoretical Research on Selective Exposure to Information, J. Pers. Soc. Psychol. 80, 557 (2001). · DOI · Paper page
  4. A. Bessi, M. Coletto, G. A. Davidescu, A. Scala, G. Caldarelli, and W. Quattrociocchi, Science vs Conspiracy: Collective Narratives in the Age of Misinformation, PLoS One 10, e0118093 (2015). · DOI · Paper page
  5. N. Grinberg, K. Joseph, L. Friedland, B. Swire-Thompson, and D. Lazer, Fake News on Twitter during the 2016 US Presidential Election, Science 363, 374 (2019). · DOI · Paper page
  6. K. Lewis, M. Gonzalez, and J. Kaufman, Social Selection and Peer Influence in an Online Social Network, Proc. Natl. Acad. Sci. U.S.A. 109, 68 (2012). · DOI
  7. J.-P. Onnela and F. Reed-Tsochas, Spontaneous Emergence of Social Influence in Online Systems, Proc. Natl. Acad. Sci. · DOI
  8. U.S.A. 107, 18375 (2010). · DOI
  9. X. Wang, W. Li, L. Liu, S. Pei, S. Tang, and Z. Zheng, Promoting Information Diffusion through Interlayer Recovery Processes in Multiplex Networks, Phys. Rev. E 96, 032304 (2017). · DOI
  10. J. Kleinberg, Analysis of Large-Scale Social and Informa- tion Networks, Phil. Trans. R. Soc. A 371, 20120378 (2013). · DOI
  11. W. Quattrociocchi, R. Conte, and E. Lodi, Opinions Manipulation: Media, Power and Gossip, Adv. Complex Syst. 14, 567 (2011). · DOI
  12. J. Ugander, L. Backstrom, C. Marlow, and J. Kleinberg, Structural Diversity in Social Contagion, Proc. Natl. Acad. Sci. U.S.A. 109, 5962 (2012). · DOI
  13. D. Lazer, The Rise of the Social Algorithm, Science 348, 1090 (2015). · DOI · Paper page
  14. E. Bakshy, S. Messing, and L. A. Adamic, Exposure to Ideologically Diverse News and Opinion on Facebook, Science 348, 1130 (2015). · DOI · Paper page
  15. C. Castellano, S. Fortunato, and V. Loreto, Statistical Physics of Social Dynamics, Rev. Mod. Phys. 81, 591 (2009). · DOI · Paper page
  16. L. Liu, X. Wang, Y. Zheng, W. Fang, S. Tang, and Z. Zheng, Homogeneity Trend on Social Networks Changes Evolu- tionary Advantage in Competitive Information Diffusion, New J. Phys. 22, 013019 (2020). · DOI
  17. C. Wardle and H. Derakhshan, Information Disorder: Toward an Interdisciplinary Framework for Research and Policymaking, Council of Europe report, DGI, Vol. 9, 2017.
  18. K. Garimella, G. De Francisci Morales, A. Gionis, and M. Mathioudakis, Political Discourse on Social Media: Echo Chambers, Gatekeepers, and the Price of Bipartisanship, in Proceedings of the 2018 World Wide Web Conference on World Wide Web (International World Wide Web Confer- ences Steering Committee, Republic and Canton of Geneva, Switzerland, 2018), pp. 913-922.
  19. E. Colleoni, A. Rozza, and A. Arvidsson, Echo Chamber or Public Sphere? Predicting Political Orientation and Measuring Political Homophily in Twitter Using Big Data, J. Commun. 64, 317 (2014). · DOI · Paper page
  20. A. Friggeri, L. Adamic, D. Eckles, and J. Cheng, Rumor Cascades, in Proceedings of the Eighth International AAAI Conference on Weblogs and Social Media (Association for the Advancement of Artificial Intelligence, AAAI, 2014), pp. 101-110.
  21. F. Fu and L. Wang, Coevolutionary Dynamics of Opinions and Networks: From Diversity to Uniformity, Phys. Rev. E 78, 016104 (2008). · DOI
  22. F. Zollo, P. K. Novak, M. Del Vicario, A. Bessi, I. Mozetič, A. Scala, G. Caldarelli, and W. Quattrociocchi, Emotional Dynamics in the Age of Misinformation, PLoS One 10, e0138740 (2015). · DOI · Paper page
  23. J. G. Webster and T. B. Ksiazek, The Dynamics of Audience Fragmentation: Public Attention in an Age of Digital Media, J. Commun. 62, 39 (2012). · DOI
  24. A. Bessi, F. Zollo, M. Del Vicario, A. Scala, G. Caldarelli, and W. Quattrociocchi, Trend of Narratives in the Age of Misinformation, PLoS One 10, e0134641 (2015). · DOI · Paper page
  25. D. Mocanu, L. Rossi, Q. Zhang, M. Karsai, and W. Quattrociocchi, Collective Attention in the Age of (Mis) Information, Comput. Human Behav. 51, 1198 (2015). · DOI
  26. A. L. Schmidt, F. Zollo, M. Del Vicario, A. Bessi, A. Scala, G. Caldarelli, H. E. Stanley, and W. Quattrociocchi, Anatomy of News Consumption on Facebook, Proc. Natl. Acad. Sci. U.S.A. 114, 3035 (2017). · DOI · Paper page
  27. D. DellaPosta, Y. Shi, and M. Macy, Why Do Liberals Drink Lattes?, Am. J. Sociology 120, 1473 (2015). · DOI · Paper page
  28. A. Goldberg and S. K. Stein, Beyond Social Contagion: Associative Diffusion and the Emergence of Cultural Variation, Am. Sociol. Rev. 83, 897 (2018). · DOI
  29. N. E. Friedkin, A Structural Theory of Social Influence (Cambridge University Press, Cambridge, England, 2006), Vol. 13.
  30. N. E. Friedkin and E. C. Johnsen, Social Influence Network Theory: A Sociological Examination of Small Group Dynamics (Cambridge University Press, Cambridge, England, 2011), Vol. 33.
  31. S. E. Parsegov, A. V. Proskurnikov, R. Tempo, and N. E. Friedkin, Novel Multidimensional Models of Opinion Dynamics in Social Networks, IEEE Trans. Autom. Control 62, 2270 (2017). · DOI
  32. N. E. Friedkin, A. V. Proskurnikov, R. Tempo, and S. E. Parsegov, Network Science on Belief System Dynamics under Logic Constraints, Science 354, 321 (2016). · DOI
  33. G. Deffuant, D. Neau, F. Amblard, and G. Weisbuch, Mixing Beliefs among Interacting Agents, Adv. Complex Syst. 03, 87 (2000). · DOI · Paper page
  34. M. Del Vicario, A. Scala, G. Caldarelli, H. E. Stanley, and W. Quattrociocchi, Modeling Confirmation Bias and Polarization, Sci. Rep. 7, 40391 (2017). · DOI
  35. P. Holme and M. E. J. Newman, Nonequilibrium Phase Transition in the Coevolution of Networks and Opinions, Phys. Rev. E 74, 056108 (2006). · DOI · Paper page
  36. K. Sasahara, W. Chen, H. Peng, G. L. Ciampaglia, A. Flammini, and F. Menczer, On the Inevitability of Online Echo Chambers, arXiv:1905.03919 [ · arXiv
  37. F. Baumann, P. Lorenz-Spreen, I. M. Sokolov, and M. Starnini, Modeling Echo Chambers and Polarization Dynamics in Social Networks, Phys. Rev. Lett. 124, 048301 (2020). · DOI · Paper page
  38. P. Barberá, J. T. Jost, J. Nagler, J. A. Tucker, and R. Bonneau, Tweeting from Left to Right: Is Online Political Communication More than an Echo Chamber?, Psychol. Sci. 26, 1531 (2015). · DOI · Paper page
  39. See Supplemental Material at http://link.aps.org/ supplemental/10.1103/PhysRevX.10.041042 for Figs. S1- S7 and Movies S1-S3: time evolutions of the three network topologies shown in Figs. 2(c)-2(e).
  40. J. Lorenz, Continuous Opinion Dynamics under Bounded Confidence: A Survey, Int. J. Mod. Phys. C 18, 1819 (2007). · DOI
  41. H. Hotelling, Stability in Competition, in The Collected Economics Articles of Harold Hotelling (Springer, New York, 1990), pp. 50-63.
  42. S. Galam and F. Jacobs, The Role of Inflexible Minorities in the Breaking of Democratic Opinion Dynamics, Physica (Amsterdam) 381A, 366 (2007). · DOI
  43. L. Böttcher, P. Montealegre, E. Goles, and H. Gersbach, Competing Activists Political Polarization, Physica (Amsterdam) 545A, 123713 (2020). · DOI
  44. D. Centola, J. Becker, D. Brackbill, and A. Baronchelli, Experimental Evidence for Tipping Points in Social Convention, Science 360, 1116 (2018). · DOI · Paper page
  45. C. B. Williams and G. J. Gulati, Digital Advertising Expend- itures in the 2016 Presidential Election, Social Science Computer Review 36, 406 (2018). · DOI
  46. A. Kucharski, Post-Truth: Study Epidemiology of Fake News, Nature (London) 540, 525 (2016). · DOI
  47. A. Bovet and H. A. Makse, Influence of Fake News in Twitter during the 2016 US Presidential Election, Nat. Commun. 10, 7 (2019). · DOI · Paper page
  48. P. Williamson, Take the Time and Effort to Correct Misinformation, Nature (London) 540, 171 (2016). · DOI
  49. D. M. Lazer, M. A. Baum, Y. Benkler, A. J. Berinsky, K. M. Greenhill, F. Menczer, M. J. Metzger, B. Nyhan, G. Pennycook, D. Rothschild et al., The Science of Fake News, Science 359, 1094 (2018). · DOI · Paper page
  50. M. Jacomy, T. Venturini, S. Heymann, and M. Bastian, Forceatlas2, a Continuous Graph Layout Algorithm for Handy Network Visualization Designed for the GEPHI Software, PLoS One 9, e98679 (2014). · DOI · Paper page
  51. V. D. Blondel, J.-L. Guillaume, R. Lambiotte, and E. Lefebvre, Fast Unfolding of Communities in Large Networks, J. Stat. Mech. (2008) P10008. · DOI · Paper page
  52. N. Johnson, R. Leahy, N. J. Restrepo, N. Velasquez, M. Zheng, P. Manrique, P. Devkota, and S. Wuchty, Hidden Resilience and Adaptive Dynamics of the Global Online Hate Ecology, Nature (London) 573, 261 (2019). · DOI

Cited by

Sorted by citations

Authors
Wang, X., Sirianni, A. D., Tang, S., Zheng, Z., & Fu, F.
Affiliations
  • Dartmouth College (US)
  • Beihang University (CN)
ORCID
Venue
Physical Review X
Publisher
American Physical Society (APS)
Published
2020-12-01
Volume
10
Issue
4
ISSN
2160-3308 (ISSN-L)
DOI
10.1103/physrevx.10.041042
OpenAlex
W3109404721
Cited by
93
References
8
Citations by year
2026: 10 · 2025: 15 · 2024: 25 · 2023: 13 · 2022: 12 · 2021: 15 · 2020: 3
FWCI
4.47
Type
journal article
Language
en
Open access
Licence
Locations
  • Physical Review X (publishedVersion, cc-by)
  • arXiv (Cornell University) (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • arXiv (Cornell University)
Funders
  • National Natural Science Foundation of China (11871004, 11922102)
  • National Key Research and Development Program of China (2018AAA0101100)
  • Bill and Melinda Gates Foundation (OPP1217336)
  • NIH Centers of Biomedical Research Excellence (COBRE) Program (1P20GM130454)
  • Neukom CompX
  • Dartmouth Faculty Startup Fund
  • Walter & Constance Burke Research Initiation
Topics
  • Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Opinion Dynamics and Social Influence
  • Social Sciences › Communication › Social Media and Politics
  • Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
Keywords
echo chambers, political polarization, social media, cognitive biases, political campaigns, opinion dynamics, social influence, radicalization, moderates, retweet networks, 2016 US presidential election, agent-based modeling, public discourse

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