Political Polarization on Twitter
Open accessAbstract
This is the training data used to produce the results shown in the paper listed below. Source: Sampled public tweets from Twitter streaming API. Date range: 6 weeks prior to the 2010 Congressional midterm elections. Contains: Three networks of political communication between Twitter users Please cite: Michael Conover, Jacob Ratkiewicz, Matthew Francisco, Bruno Goncalves, Alessandro Flammini, and Filippo Menczer. Political Polarization on Twitter. Proc. 5th International AAAI Conference on Weblogs and Social Media ICWSM, 2011.
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Retweets tend to represent ideological endorsements, while mentions more often represent ideological opposition.
“Retweets and mentions can serve different communicative purposes on Twitter with distinct ideological implications; in particular, Conover et al. (2011) have suggested that retweets tend to represent ideological endorsements, while mentions more often represent ideological opposition.”
From #Ferguson is everywhere: initiators in emerging counterpublic networks (2016) · cites this paper as Conover et al. (2011)
Conover et al. found that users are more likely to interact with people with whom they agree, with odds ratios of 1.2-1.3 for mentions and 1.7-2.3 for retweets, concluding that the retweet network is highly polarized.
“they find that users are more likely to interact people with whom they agree, with an odds ratio of 1.2–1.3 for mentions and 1.7–2.3 for retweets, thus concluding that the retweet network is highly polarized.”
From No echo in the chambers of political interactions on Reddit (2021) · cites this paper as [4]
Homophily in communication is a key tendency of users in information propagation media like Twitter and other platforms: people tend to communicate with others who hold similar beliefs.
“Homophily in communication is a key tendency of users in information propagation media like Twitter and other platforms: people tend to communicate with others who hold similar beliefs (Barberá, 2015;Conover et al., 2011).”
From Credibility of climate change denial in social media (2019) · cites this paper as Conover et al., 2011
Cites
The Measurement of Observer Agreement for Categorical Data
Landis, J. R., & Koch, G. G. (1977) · Biometrics · doi:10.2307/2529310 · Cited by 81,148 · see citing works
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Bail, C. A., Argyle, L. P., Brown, T. W., Bumpus, J. P., Chen, H., Hunzaker, M. B. F., Lee, J., Mann, M., Merhout, F., & Volfovsky, A. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1804840115 · Cited by 1,863 · see citing works
Emotions and Information Diffusion in Social Media—Sentiment of Microblogs and Sharing Behavior
Stieglitz, S., & Dang-Xuan, L. (2013) · Journal of Management Information Systems · doi:10.2753/mis0742-1222290408 · Cited by 1,604 · 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 · see citing works
Political Effects of the Internet and Social Media
Zhuravskaya, E., Petrova, M., & Enikolopov, R. (2020) · Annual Review of Economics · doi:10.1146/annurev-economics-081919-050239 · Cited by 560 · see citing works
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Stella, M., Ferrara, E., & De Domenico, M. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1803470115 · Cited by 492 · see citing works
Mohammad, S. M. (2016) · Emotion Measurement · doi:10.1016/b978-0-08-100508-8.00009-6 · Cited by 391 · see citing works
How digital media drive affective polarization through partisan sorting
Törnberg, P. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2207159119 · Cited by 288 · see citing works
#Ferguson is everywhere: initiators in emerging counterpublic networks
Jackson, S. J., & Foucault Welles, B. (2016) · Information, Communication & Society · doi:10.1080/1369118x.2015.1106571 · Cited by 276 · see citing works
Assessing the bias in samples of large online networks
González-Bailón, S., Wang, N., Rivero, A., Borge-Holthoefer, J., & Moreno, Y. (2014) · Social Networks · doi:10.1016/j.socnet.2014.01.004 · Cited by 238 · see citing works
Information gerrymandering and undemocratic decisions
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Gayo-Avello, D. (2012) · arXiv · doi:10.48550/arxiv.1204.6441 · Cited by 179 · see citing works
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Hosseinmardi, H., Ghasemian, A., Clauset, A., Mobius, M., Rothschild, D. M., & Watts, D. J. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2101967118 · Cited by 171 · see citing works
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Demszky, D., Garg, N., Voigt, R., Zou, J., Shapiro, J., Gentzkow, M., & Jurafsky, D. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1304 · Cited by 164 · see citing works
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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 · see citing works
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Kulshrestha, J., Eslami, M., Messias, J., Zafar, M. B., Ghosh, S., Gummadi, K. P., & Karahalios, K. (2017) · Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing · doi:10.1145/2998181.2998321 · Cited by 148 · see citing works
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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
Flamino, J., Galeazzi, A., Feldman, S., Macy, M. W., Cross, B., Zhou, Z., Serafino, M., Bovet, A., Makse, H. A., & Szymanski, B. K. (2023) · Nature Human Behaviour · doi:10.1038/s41562-023-01550-8 · Cited by 113 · see citing works
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De Francisci Morales, G., Monti, C., & Starnini, M. (2021) · Scientific Reports · doi:10.1038/s41598-021-81531-x · Cited by 98 · see citing works
Hannak, A., Margolin, D., Keegan, B., & Weber, I. (2014) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v8i1.14555 · Cited by 90 · see citing works
The Web of False Information: Rumors, Fake News, Hoaxes, Clickbait, and Various Other Shenanigans
Zannettou, S., Sirivianos, M., Blackburn, J., & Kourtellis, N. (2019) · Journal of Data and Information Quality · doi:10.1145/3309699 · Cited by 86 · see citing works
The role of hidden influentials in the diffusion of online information cascades
Baños, R. A., Borge-Holthoefer, J., & Moreno, Y. (2013) · EPJ Data Science · doi:10.1140/epjds18 · Cited by 79 · see citing works
Communication power struggles on social media: A case study of the 2011–12 Russian protests
Spaiser, V., Chadefaux, T., Donnay, K., Russmann, F., & Helbing, D. (2017) · Journal of Information Technology & Politics · doi:10.1080/19331681.2017.1308288 · Cited by 77 · see citing works
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Samantray, A., & Pin, P. (2019) · Palgrave Communications · doi:10.1057/s41599-019-0344-4 · Cited by 60 · see citing works
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Wu, B., Liu, L., Yang, Y., Zheng, K., & Wang, X. (2020) · IEEE Access · doi:10.1109/access.2020.2975630 · Cited by 54 · see citing works
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Rivero, G. (2019) · Contemporary Social Science · doi:10.1080/21582041.2017.1325924 · Cited by 26 · see citing works
López-Robles, A. (2023) · Revista de la Asociación Española de Investigación de la Comunicación · doi:10.24137/raeic.10.19.12 · Cited by 1 · see citing works
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- Authors
- Conover, M., Ratkiewicz, J., Francisco, M., Goncalves, B., Flammini, A., & Menczer, F.
- Affiliations
- Indiana University Bloomington (US)
- Center for Complex Networks and Systems Research, School of Informatics and Computing, Indiana University, Bloomington
- Venue
- Zenodo
- Publisher
- Zenodo
- Published
- 2011-07-05
- OpenAlex
- W4394031805
- Cited by
- 1,254
- References
- 1
- Citations by year
- 2026: 2 · 2025: 17 · 2024: 22 · 2023: 47 · 2022: 63 · 2021: 199 · 2020: 140 · 2019: 151 · 2018: 109 · 2017: 116 · 2016: 90 · 2015: 88 · 2014: 91 · 2013: 71 · 2012: 40
- Type
- dataset
- Language
- en
- Open access
- green
- doi.org
- Licence
- other-oa
- Locations
- Zenodo (CERN European Organization for Nuclear Research) (other-oa)
- Topics
- Social Sciences › Communication › Social Media and Politics
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Opinion Dynamics and Social Influence
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Keywords
- political polarization, Twitter, social networks, political communication, 2010 midterm elections, user networks, online political discourse
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