Partisan asymmetries in online political activity
Open accessAbstract
We examine partisan differences in the behavior, communication patterns and social interactions of more than 18,000 politically-active Twitter users to produce evidence that points to changing levels of partisan engagement with the American online political landscape. Analysis of a network defined by the communication activity of these users in proximity to the 2010 midterm congressional elections reveals a highly segregated, well clustered, partisan community structure. Using cluster membership as a high-fidelity (87% accuracy) proxy for political affiliation, we characterize a wide range of differences in the behavior, communication and social connectivity of left- and right-leaning Twitter users. We find that in contrast to the online political dynamics of the 2008 campaign, right-leaning Twitter users exhibit greater levels of political activity, a more tightly interconnected social structure, and a communication network topology that facilitates the rapid and broad dissemination of political information.
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The topology of the network of right-leaning Twitter users facilitates the rapid and broad dissemination of political information and increases their potential to manipulate the public agenda.
“As Conover et al. (2012) argue, the topology of the network of right-leaning Twitter users facilitates the rapid and broad dissemination of political information, and increases their potential to manipulate the public agenda.”
From Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data (2015) · cites this paper as Conover et al. 2012
The Introduction and Conclusion state that right-leaning users’ network topology facilitates the rapid and broad dissemination of political information. Section 5.3 also suggests more opportunities for political frame-making, but the paper does not say that this increases their potential to manipulate the public agenda. (LLM, conf=0.85)
Experimental — accuracy not yet measuredMultiple studies have observed patterns of homophilic segregation consistent with these models in networks of interactions between Twitter users.
“Multiple studies have observed patterns of homophilic segregation consistent with these models in networks of interactions between Twitter users (Wu et al. 2011; Conover et al. 2012).”
From Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data (2015) · cites this paper as Conover et al. 2012
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Cites
Users of the world, unite! The challenges and opportunities of Social Media
Kaplan, A. M., & Haenlein, M. (2010) · Business Horizons · doi:10.1016/j.bushor.2009.09.003 · Cited by 17,875 · see citing works
Power-Law Distributions in Empirical Data
Clauset, A., Shalizi, C. R., & Newman, M. E. J. (2009) · SIAM Review · doi:10.1137/070710111 · Cited by 8,019 · see citing works
What is Twitter, a social network or a news media?
Kwak, H., Lee, C., Park, H., & Moon, S. (2010) · Proceedings of the 19th international conference on World wide web · doi:10.1145/1772690.1772751 · Cited by 6,652 · see citing works
An introduction to latent semantic analysis
Landauer, T. K., Foltz, P. W., & Laham, D. (1998) · Discourse Processes · doi:10.1080/01638539809545028 · Cited by 4,880 · see citing works
Finding community structure in networks using the eigenvectors of matrices
Newman, M. E. J. (2006) · Physical Review E · doi:10.1103/physreve.74.036104 · Cited by 4,799 · see citing works
Near linear time algorithm to detect community structures in large-scale networks
Raghavan, U. N., Albert, R., & Kumara, S. R. T. (2007) · Physical Review E · doi:10.1103/physreve.76.036106 · Cited by 3,535 · see citing works
Lazer, D., Pentland, A., Adamic, L., Aral, S., Barabási, A.-L., Brewer, D., Christakis, N., Contractor, N., Fowler, J., Gutmann, M., et al. (2009) · Science · doi:10.1126/science.1167742 · Cited by 3,365 · see citing works
Identification of influential spreaders in complex networks
Kitsak, M., Gallos, L. K., Havlin, S., Liljeros, F., Muchnik, L., Stanley, E., & Makse, H. A. (2010) · Nature Physics · doi:10.1038/nphys1746 · Cited by 3,224 · see citing works
Dynamical Processes on Complex Networks
Barrat, A., Barthélemy, M., & Vespignani, A. (2008) · doi:10.1017/cbo9780511791383 · Cited by 2,704 · cited 1× · see citing works
“The shell index, c, of a node refers to the coreness (k) of the highest-order k-core of which the node is a member (Barrat et al., 2008).”
BackgroundThe 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
Kolbe, R. H., & Burnett, M. S. (1991) · Journal of Consumer Research · doi:10.1086/209256 · Cited by 1,516 · see citing works
Romero, D. M., Meeder, B., & Kleinberg, J. (2011) · Proceedings of the 20th international conference on World wide web · doi:10.1145/1963405.1963503 · Cited by 1,082 · see citing works
Protest in an Information Society: a review of literature on social movements and new ICTs
Garrett, R. (2006) · Information Communication & Society · doi:10.1080/13691180600630773 · Cited by 789 · see citing works
Unraveling the Effects of the Internet on Political Participation?
Tolbert, C. J., & McNeal, R. (2003) · Political Research Quarterly · doi:10.1177/106591290305600206 · Cited by 642 · see citing works
Contesting media power: alternative media in a networked world
Couldry, N., & Curran, J. (2003) · Cited by 594 · see citing works
The power and politics of blogs
Farrell, H. J., & Drezner, D. W. (2007) · Public Choice · doi:10.1007/s11127-007-9198-1 · Cited by 567 · see citing works
Flagged first, then by citations
Research works printed but not linked: 8
- Benkler Y (2006) The wealth of networks: how social production transforms markets and freedom. Yale University Press, New Haven
- Sunstein CR (2007) Republic.com 2.0. Princeton University Press, Princeton
- Pew Internet and American Life Project (2008) Social networking and online videos take off: Internet's broader role in campaign 2008. Technical report, Pew Research Center. http://people-press.org/reports/display.php3?ReportID=384
- Conover M, Ratkiewicz J, Francisco M, Gonçalves B, Flammini A, Menczer F (2011) Political polarization on Twitter. In: Fifth international AAAI conference on weblogs and social media, p 89
- Java A, Song X, Finin T, Tseng B (2007) Why we Twitter: understanding microblogging usage and communities. In: Proc. of the 9th WebKDD and 1st SNA-KDD 2007 workshop on Web mining and social network analysis 22. Boyd D, Golder S, Lotan G (2008) Tweet, tweet, retweet: conversational aspects of retweeting on Twitter. In: Proc. Hawaii intl. conf. on systems sciences, pp 1-10
- Honeycutt C, Herring SC (2008) Beyond microblogging: conversation and collaboration via Twitter. In: Proc. 42nd Hawaii intl conf. on system sciences
- Ratkiewicz J, Conover M, Meiss M, Gonçalves B, Patil S, Flammini A, Menczer F (2011) Truthy: mapping the spread of astroturf in microblog streams. In: Proc. 20th intl. World Wide Web conf. (WWW)
- Conover M, Ratkiewicz J, Gonçalves B, Haff J, Flammini A, Menczer F (2011) Predicting the political alignment of Twitter users. In: Proceedings of 3rd IEEE conference on social computing (SocialCom)
Cited by
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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
The spread of low-credibility content by social bots
Shao, C., Ciampaglia, G. L., Varol, O., Yang, K.-C., Flammini, A., & Menczer, F. (2018) · Nature Communications · doi:10.1038/s41467-018-06930-7 · Cited by 1,125 · see citing works
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Barberá, P. (2015) · Political Analysis · doi:10.1093/pan/mpu011 · Cited by 982 · see citing works
Twitter use in election campaigns: A systematic literature review
Jungherr, A. M. (2015) · Journal of Information Technology & Politics · doi:10.1080/19331681.2015.1132401 · Cited by 802 · see citing works
Network analysis reveals open forums and echo chambers in social media discussions of climate change
Williams, H. T. P., McMurray, J. R., Kurz, T., & Hugo Lambert, F. (2015) · Global Environmental Change · doi:10.1016/j.gloenvcha.2015.03.006 · Cited by 631 · see citing works
Understanding the Political Representativeness of Twitter Users
Barberá, P., & Rivero, G. (2014) · Social Science Computer Review · doi:10.1177/0894439314558836 · Cited by 379 · see citing works
Social Media, Echo Chambers, and Political Polarization
Barberá, P. (2020) · Social Media and Democracy · doi:10.1017/9781108890960.004 · Cited by 373 · see citing works
More Tweets, More Votes: Social Media as a Quantitative Indicator of Political Behavior
DiGrazia, J., McKelvey, K., Bollen, J., & Rojas, F. G. (2013) · PLoS ONE · doi:10.1371/journal.pone.0079449 · Cited by 334 · see citing works
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Margolin, D., Hannák, A., & Weber, I. G. (2017) · Political Communication · doi:10.1080/10584609.2017.1334018 · Cited by 283 · see citing works
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Shao, C., Hui, P.-M., Wang, L., Jiang, X., Flammini, A., Menczer, F., & Ciampaglia, G. L. (2018) · PLoS ONE · doi:10.1371/journal.pone.0196087 · Cited by 282 · see citing works
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Jost, J. T., Baldassarri, D. S., & Druckman, J. N. (2022) · Nature Reviews Psychology · doi:10.1038/s44159-022-00093-5 · Cited by 276 · see citing works
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Sasahara, K., Chen, W., Peng, H., Ciampaglia, G. L., Flammini, A., & Menczer, F. (2021) · Journal of Computational Social Science · doi:10.1007/s42001-020-00084-7 · Cited by 211 · see citing works
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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
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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
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Gallagher, R. J., Reagan, A. J., Danforth, C. M., & Dodds, P. S. (2018) · PLoS ONE · doi:10.1371/journal.pone.0195644 · Cited by 157 · see citing works
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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
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Caldarelli, G., Nicola, R. D., Vigna, F. D., Petrocchi, M., & Saracco, F. (2020) · Communications Physics · doi:10.1038/s42005-020-0340-4 · Cited by 135 · see citing works
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Beauchamp, N. (2016) · American Journal of Political Science · doi:10.1111/ajps.12274 · Cited by 132 · see citing works
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Vergeer, M. (2015) · Sociology Compass · doi:10.1111/soc4.12294 · Cited by 130 · see citing works
Bovet, A., Morone, F., & Makse, H. A. (2018) · Scientific Reports · doi:10.1038/s41598-018-26951-y · Cited by 135 · see citing works
Halpern, D., Valenzuela, S., Katz, J., & Miranda, J. P. (2019) · Interacción · doi:10.1007/978-3-030-21902-4_16 · Cited by 94 · see citing works
Siegel, A. A. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108890960.005 · Cited by 117 · see citing works
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Jungherr, A. M. (2014) · SSRN Electronic Journal · doi:10.2139/ssrn.2402443 · Cited by 112 · see citing works
Do social media undermine social cohesion? A critical review
González-Bailón, S., & Lelkes, Y. (2022) · Social Issues and Policy Review · doi:10.1111/sipr.12091 · Cited by 110 · see citing works
Context matters: political polarization on Twitter from a comparative perspective
Urman, A. (2020) · Media, Culture & Society · doi:10.1177/0163443719876541 · Cited by 108 · see citing works
Steinert-Threlkeld, Z. C. (2018) · Cambridge University Press eBooks · doi:10.1017/9781108529327 · Cited by 121 · see citing works
Aragón, P., Kappler, K. E., Kaltenbrunner, A., Laniado, D., & Volkovich, Y. (2013) · Policy & Internet · doi:10.1002/1944-2866.poi327 · Cited by 123 · see citing works
Ideological and Temporal Components of Network Polarization in Online Political Participatory Media
García, D., Abisheva, A., Schweighofer, S., Serdült, U., & Schweitzer, F. (2015) · Policy & Internet · doi:10.1002/poi3.82 · Cited by 91 · see citing works
Wojcieszak, M., Casas, A., Yu, X., Nagler, J., & Tucker, J. A. (2022) · Science Advances · doi:10.1126/sciadv.abn9418 · Cited by 90 · see citing works
Hate in a Tweet: Exploring Internet-Based Islamophobic Discourses
Evolvi, G. (2018) · Religions · doi:10.3390/rel9100307 · Cited by 83 · see citing works
McGregor, S. C., Mourão, R. R., & Molyneux, L. (2017) · Journal of Information Technology & Politics · doi:10.1080/19331681.2017.1308289 · Cited by 82 · see citing works
Topology comparison of Twitter diffusion networks effectively reveals misleading information
Pierri, F., Piccardi, C., & Ceri, S. (2020) · Scientific Reports · doi:10.1038/s41598-020-58166-5 · Cited by 85 · see citing works
Understanding Popularity, Reputation, and Social Influence in the Twitter Society
García, D., Mavrodiev, P., Casati, D., & Schweitzer, F. (2017) · Policy & Internet · doi:10.1002/poi3.151 · Cited by 80 · see citing works
The Role of Social Media in the Discussion of Controversial Topics
Smith, L. M., Zhu, L., Lerman, K., & Kozareva, Z. (2013) · International Conference on Social Computing · doi:10.1109/socialcom.2013.41 · Cited by 71 · see citing works
Rao, A., Morstatter, F., Hu, M., Chen, E., Burghardt, K., Ferrara, E., & Lerman, K. (2021) · Journal of Medical Internet Research · doi:10.2196/26692 · Cited by 80 · see citing works
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Williams, N. L., Inversini, A., Buhalis, D., & Ferdinand, N. (2015) · Journal of Marketing Management · doi:10.1080/0267257x.2015.1035308 · Cited by 68 · see citing works
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Alizadeh, M., Weber, I. G., Cioffi‐Revilla, C. A., Fortunato, S., & Macy, M. W. (2019) · EPJ Data Science · doi:10.1140/epjds/s13688-019-0193-9 · Cited by 65 · see citing works
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Peng, T., Liu, M., Wu, Y., & Liu, S. (2014) · Communication Research · doi:10.1177/0093650214559601 · Cited by 60 · see citing works
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Reed, M. (2016) · Space and Polity · doi:10.1080/13562576.2016.1192332 · Cited by 34 · see citing works
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Mining and modelling temporal dynamics of followers’ engagement on online social networks
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Green, J., McCabe, S., Shugars, S., Chwe, H., Horgan, L., CAO, S., & Lazer, D. M. J. (2025) · American Political Science Review · doi:10.1017/s0003055424000984 · Cited by 22 · see citing works
Conclusion: The Challenges and Opportunities for Social Media Research
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Self-citationSustainable development goals as unifying narratives in large UK firms’ Twitter discussions
Patuelli, A., & Saracco, F. (2023) · Scientific Reports · doi:10.1038/s41598-023-34024-y · Cited by 26 · see citing works
When the whole world is watching: A motivations-based account of selective expression and exposure
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Opinion Polarization during a Dichotomous Electoral Process
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Gajewski, Ł. G., Sienkiewicz, J., & Hołyst, J. A. (2022) · Physical review. E · doi:10.1103/physreve.105.024125 · Cited by 19 · see citing works
- Authors
- Conover, M. D., Gonçalves, B., Flammini, A., & Menczer, F.
- Affiliations
- Indiana University Bloomington (US)
- Northeastern University (US)
- Corresponding author
- Gonçalves, Bruno
- Venue
- EPJ Data Science
- Published
- 2012-06-18
- Volume
- 1
- Issue
- 1
- ISSN
- 2193-1127 (ISSN-L)
- OpenAlex
- W2005499496
- Cited by
- 234
- References
- 16
- Citations by year
- 2026: 9 · 2025: 16 · 2024: 17 · 2023: 19 · 2022: 19 · 2021: 23 · 2020: 25 · 2019: 38 · 2018: 14 · 2017: 16 · 2016: 12 · 2015: 14 · 2014: 6 · 2013: 4 · 2012: 2
- FWCI
- 26.85
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- EPJ Data Science (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Social Sciences › Communication › Social Media and Politics
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Keywords
- partisan asymmetry, online political activity, Twitter, 2010 midterm elections, political affiliation, social network structure, information dissemination
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 ›
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