What Do Retweets Indicate? Results from User Survey and Meta-Review of Research
Open accessAbstract
Arguably one of the most important features of Twitter is the support for “retweets” or messages re-posted verbatim by a user that were originated by someone else. Despite the fact that retweets are routinely studied and reported, many important questions remain about user motivation for their use and their significance. In this paper we answer the question of what users indicate when they retweet. We do so in a comprehensive fashion, by employing a user survey, a study of user profiles, and a meta-review of over 100 research publications from three related major conferences. Our findings indicate that retweeting indicates not only interest in a message, but also trust in the message and the originator, and agreement with the message contents. However, the findings are significantly weaker for journalists, some of whom beg to differ declaring so in their own user profiles. On the other hand, the inclusion of hashtags strengthens the signal of agreement, especially when the hashtags are related to politics. While in the past there have been additional claims in the literature about possible reasons for retweeting, many of them are not supported, especially given the technical changes introduced recently by Twitter.
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Retweets are traditionally considered as a form of social endorsement: Users tend to retweet and thus endorse content they agree with.
“While Mentions and Replies can have both negative and positive connotations, Retweets are traditionally considered as a form of social endorsement [26, 17]: Users tend to retweet and thus endorse content they agree with.”
From Influence of augmented humans in online interactions during voting events (2019) · cites this paper as [26]
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Cites
Tweet, Tweet, Retweet: Conversational Aspects of Retweeting on Twitter
Boyd, D., Golder, S., & Lotan, G. (2010) · 2010 43rd Hawaii International Conference on System Sciences · doi:10.1109/hicss.2010.412 · Cited by 2,051 · see citing works
The Arab Spring and Social Media Audiences
Bruns, A., Highfield, T., & Burgess, J. (2013) · American Behavioral Scientist · doi:10.1177/0002764213479374 · Cited by 283 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Bots increase exposure to negative and inflammatory content in online social systems
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
Scientists on Twitter: Preaching to the choir or singing from the rooftops?
Côté, I. M., & Darling, E. S. (2018) · FACETS · doi:10.1139/facets-2018-0002 · Cited by 195 · see citing works
Brady, W. J., Wills, J. A., Burkart, D., Jost, J. T., & Van Bavel, J. J. (2019) · Journal of Experimental Psychology: General · doi:10.1037/xge0000532 · Cited by 188 · see citing works
Pausing to consider why a headline is true or false can help reduce the sharing of false news
Fazio, L. (2020) · Harvard Kennedy School Misinformation Review · doi:10.37016/mr-2020-009 · Cited by 187 · see citing works
Detecting and Characterizing Eating-Disorder Communities on Social Media
Wang, T., Brede, M., Ianni, A., & Mentzakis, E. (2017) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/3018661.3018706 · Cited by 114 · 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
How does consumer engagement evolve when brands post across multiple social media?
Unnava, V., & Aravindakshan, A. (2021) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-021-00785-z · Cited by 110 · 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
The Twitter parliamentarian database: Analyzing Twitter politics across 26 countries
van Vliet, L., Törnberg, P., & Uitermark, J. (2020) · PLoS ONE · doi:10.1371/journal.pone.0237073 · Cited by 93 · see citing works
Retweeting: its linguistic and epistemic value
Marsili, N. (2020) · Synthese · doi:10.1007/s11229-020-02731-y · Cited by 80 · see citing works
Lueders, A., Dinkelberg, A., & Quayle, M. (2022) · Acta Psychologica · doi:10.1016/j.actpsy.2022.103643 · Cited by 104 · see citing works
Luceri, L., Giordano, S., & Ferrara, E. (2020) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v14i1.7311 · Cited by 72 · see citing works
Thelwall, M., & Thelwall, S. (2020) · Aslib Journal of Information Management · doi:10.1108/ajim-05-2020-0134 · Cited by 69 · see citing works
Polarization of climate politics results from partisan sorting: Evidence from Finnish Twittersphere
Chen, T. H. Y., Salloum, A., Gronow, A., Ylä‐Anttila, T., & Kivelä, M. (2021) · Global Environmental Change · doi:10.1016/j.gloenvcha.2021.102348 · Cited by 65 · see citing works
Narratives of the Refugee Crisis: A Comparative Study of Mainstream-Media and Twitter
Nerghes, A., & Lee, J.-. (2019) · Media and Communication · doi:10.17645/mac.v7i2.1983 · Cited by 59 · see citing works
Influence of augmented humans in online interactions during voting events
Stella, M., Cristoforetti, M., & De Domenico, M. (2019) · PLOS ONE · doi:10.1371/journal.pone.0214210 · Cited by 51 · see citing works
“[22] Panagiotis Takis Metaxas, Eni Mustafaraj, Kily Wong, Laura Zeng, Megan O’Keefe, and Samantha Finn.”
BackgroundMethodInfluentialGarimella, K., Weber, I. G., & Choudhury, M. D. (2016) · ACM Web Science Conference (WebSci) · doi:10.1145/2908131.2908170 · Cited by 49 · see citing works
Social Media Sustainability Communication: An Analysis of Firm Behaviour and Stakeholder Responses
Jha, A. K., & Verma, N. K. (2022) · Information Systems Frontiers · doi:10.1007/s10796-022-10257-6 · Cited by 55 · see citing works
Partisan asymmetries in exposure to misinformation
Rao, A., Morstatter, F., & Lerman, K. (2022) · Scientific Reports · doi:10.1038/s41598-022-19837-7 · Cited by 44 · see citing works
Community Detection in Political Discussions on Twitter
Guerrero-Solé, F. (2015) · Social Science Computer Review · doi:10.1177/0894439315617254 · Cited by 44 · see citing works
“I agree with you, bot!” How users (dis)engage with social bots on Twitter
Wischnewski, M., Ngo, T., Bernemann, R., Jansen, M., & Krämer, N. C. (2022) · New Media & Society · doi:10.1177/14614448211072307 · Cited by 46 · see citing works
Bridgman, A., Merkley, E., Zhilin, O., Loewen, P. J., Owen, T., & Ruths, D. A. (2021) · Frontiers in Political Science · doi:10.3389/fpos.2021.648646 · Cited by 43 · see citing works
The (Mis)Information Game: A social media simulator
Butler, L. H., Lamont, P. X., Wan, D. L. Y., Prike, T., Nasim, M., Walker, B., Fay, N., & Ecker, U. K. H. (2023) · Behavior Research Methods · doi:10.3758/s13428-023-02153-x · Cited by 41 · see citing works
Araujo, R. F. (2020) · Scientometrics · doi:10.1007/s11192-020-03566-7 · Cited by 42 · see citing works
R-Map: A Map Metaphor for Visualizing Information Reposting Process in Social Media
Chen, S., Li, S., Chen, S., & Yuan, X. (2019) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2019.2934263 · Cited by 39 · see citing works
On analyzing user preference dynamics with temporal social networks
Pereira, F. S. F., da Gama, J. M. P., de Amo, S., & Oliveira, G. M. B. (2018) · Machine Learning · doi:10.1007/s10994-018-5740-2 · Cited by 38 · see citing works
Retweeting Behavior Prediction Based on One-Class Collaborative Filtering in Social Networks
Jiang, B., Liang, J., Sha, Y., Li, R., Liu, W., Ma, H., & Wang, L. (2016) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/2911451.2914713 · Cited by 36 · see citing works
How Politicians Learn from Citizens’ Feedback: The Case of Gender on Twitter
Schöll, N., Gallego, A., & Mens, G. L. (2023) · American Journal of Political Science · doi:10.1111/ajps.12772 · Cited by 18 · see citing works
Falkenberg, M., Zollo, F., Quattrociocchi, W., Pfeffer, J., & Baronchelli, A. (2024) · Nature Communications · doi:10.1038/s41467-024-53868-0 · Cited by 43 · see citing works
Shipping industry's sustainability communications to public in social media: A longitudinal analysis
Wang, X., Wong, Y. D., Li, K. X., & Yuen, K. F. (2021) · Transport Policy · doi:10.1016/j.tranpol.2021.05.031 · Cited by 34 · see citing works
Global halal food discourse on social media: a text mining approach
Mostafa, M. M. (2020) · Journal of International Communication · doi:10.1080/13216597.2020.1795702 · Cited by 33 · see citing works
Guerra, P., Nalon, R., Assunção, R., & Meira, W. (2017) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v11i1.14971 · Cited by 32 · see citing works
van der Linden, S., Albarracín, D., Fazio, L. K., Freelon, D., Roozenbeek, J., Swire‐Thompson, B., & Bavel, J. J. V. (2023) · PsycEXTRA Dataset · doi:10.1037/e506432023-001 · Cited by 28 · see citing works
#ArsonEmergency and Australia’s “Black Summer”: Polarisation and Misinformation on Social Media
Weber, D., Nasim, M., Falzon, L., & Mitchell, L. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-61841-4_11 · Cited by 25 · see citing works
Yuan, S., Chen, Y., Vojta, S., & Chen, Y. (2024) · New Media & Society · doi:10.1177/14614448221122202 · Cited by 23 · see citing works
Torregrosa, J., Panizo-Lledot, Á., Bello-Orgaz, G., & Camacho, D. (2020) · Social Network Analysis and Mining · doi:10.1007/s13278-020-00676-1 · Cited by 25 · see citing works
Multipolar social systems: Measuring polarization beyond dichotomous contexts
Martín-Gutíerrez, S., Losada, J. C., & Benito, R. M. (2023) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2023.113244 · Cited by 27 · see citing works
Darius, P. (2022) · Social Network Analysis and Mining · doi:10.1007/s13278-022-00958-w · Cited by 22 · see citing works
Sanford, M., Witkowska, M., Gifford, R., & Formanowicz, M. (2023) · Frontiers in Psychology · doi:10.3389/fpsyg.2022.1099331 · Cited by 25 · see citing works
Quote RTs on Twitter: Usage of the New Feature for Political DiscoursePreprint
Garimella, K., Weber, I. G., & Choudhury, M. D. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1603.07933 · Cited by 22 · see citing works
Keuchenius, A., Törnberg, P., & Uitermark, J. (2021) · PLOS ONE · doi:10.1371/journal.pone.0256696 · Cited by 27 · see citing works
Tracking Fringe and Coordinated Activity on Twitter Leading Up to the US Capitol Attack
Vishnuprasad, P. S., Nogara, G., Cardoso, F. M., Cresci, S., Giordano, S., & Luceri, L. (2024) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v18i1.31409 · Cited by 21 · see citing works
Arguing about informant credibility in open multi-agent systems
Gottifredi, S., Tamargo, L. H., García, A. J., & Simari, G. R. (2018) · Artificial Intelligence · doi:10.1016/j.artint.2018.03.001 · Cited by 20 · see citing works
Terrorist Attacks, Cultural Incidents, and the Vote for Radical Parties: Analyzing Text from Twitter
Giavazzi, F., Iglhaut, F., Lemoli, G., & Rubera, G. (2023) · American Journal of Political Science · doi:10.1111/ajps.12764 · Cited by 22 · see citing works
Reveilhac, M., & Morselli, D. (2022) · Swiss Political Science Review · doi:10.1111/spsr.12543 · Cited by 19 · see citing works
Twitter Mediated Sociopolitical Communication During the COVID-19 Pandemic Crisis in India
Jain, N., Malviya, P., Singh, P., & Mukherjee, S. (2021) · Frontiers in Psychology · doi:10.3389/fpsyg.2021.784907 · Cited by 18 · see citing works
Understanding Public Engagement With Global Aid Agencies on Twitter: A Technosocial Framework
Shahin, S., & Dai, Z. (2019) · American Behavioral Scientist · doi:10.1177/0002764219835248 · Cited by 18 · see citing works
Sainudiin, R., Yogeeswaran, K., Nash, K. A., & Sahioun, R. (2019) · Social Network Analysis and Mining · doi:10.1007/s13278-019-0567-9 · Cited by 17 · see citing works
Ecker-Ehrhardt, M. (2023) · The Review of International Organizations · doi:10.1007/s11558-023-09517-0 · Cited by 16 · see citing works
Schuchard, R., & Crooks, A. T. (2021) · PLoS ONE · doi:10.1371/journal.pone.0244309 · Cited by 14 · see citing works
Wang, E., Luceri, L., Pierri, F., & Ferrara, E. (2023) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v17i1.22197 · Cited by 15 · see citing works
Retweet Prediction Using Social-Aware Probabilistic Matrix Factorization
Jiang, B., Lü, Z., Li, N., Wu, J., & Jiang, Z. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-93698-7_24 · Cited by 15 · see citing works
Political Partisanship and Anti-Science Attitudes in Online Discussions about Covid-19Preprint
Rao, A., Morstatter, F., Hu, M., Chen, E., Burghardt, K., Ferrara, E., & Lerman, K. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2011.08498 · Cited by 15 · see citing works
Weber, D., Falzon, L., Mitchell, L., & Nasim, M. (2022) · Social Network Analysis and Mining · doi:10.1007/s13278-022-00892-x · Cited by 15 · see citing works
Engagement with news on Twitter: insights from Australia and Korea
Ackland, R., O’Neil, M., & Park, S. (2018) · Asian Journal of Communication · doi:10.1080/01292986.2018.1462393 · Cited by 14 · see citing works
Joseph, K., Swire‐Thompson, B., Masuga, H., Baum, M., & Lazer, D. M. J. (2019) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v13i01.3230 · Cited by 14 · see citing works
Social Approval and Network Homophily as Motivators of Online ToxicityPreprint
Jiang, J., Luceri, L., Walther, J. B., & Ferrara, E. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4614257 · Cited by 14 · see citing works
An empirical study on Twitter’s use and crisis retweeting dynamics amid Covid-19
Wang, B., Liu, B., & Zhang, Q. (2021) · Natural Hazards · doi:10.1007/s11069-020-04497-5 · Cited by 13 · see citing works
Misinformation on Twitter During the Danish National Election: A Case Study
Derczynski, L., Albert-Lindqvist, T. O., Bendsen, M. V., Inie, N., Pedersen, V. D., & Pedersen, J. E. (2019) · Conference for Truth and Trust Online (TTO) · doi:10.36370/tto.2019.16 · Cited by 12 · see citing works
Anti-vaccine rabbit hole leads to political representation: the case of Twitter in Japan
Toriumi, F., Sakaki, T., Kobayashi, T., & Yoshida, M. (2024) · Journal of Computational Social Science · doi:10.1007/s42001-023-00241-8 · Cited by 11 · see citing works
Sachdeva, N., Rathore, A. K., Sondhi, N., & Bamel, U. K. (2023) · Electronic Commerce Research · doi:10.1007/s10660-023-09740-2 · Cited by 12 · see citing works
Heltzel, G., & Laurin, K. (2024) · Psychological Science · doi:10.1177/09567976241258149 · Cited by 13 · see citing works
Public Health Messaging on Twitter During the COVID-19 Pandemic: Observational Study
Rao, A., Sabri, N., Guo, S., Raschid, L., & Lerman, K. (2024) · Journal of Medical Internet Research · doi:10.2196/63910 · Cited by 10 · see citing works
Polarization of Climate Politics Results from Partisan Sorting: Evidence from Finnish TwitterspherePreprint
Chen, T. H. Y., Salloum, A., Gronow, A., Ylä‐Anttila, T., & Kivelä, M. (2020) · doi:10.31235/osf.io/49qgu · Cited by 10 · see citing works
Estimating community feedback effect on topic choice in social media with predictive modeling
Adelani, D. I., Kobayashi, R., Weber, I. G., & Grabowicz, P. A. (2020) · Publications of the UdS (Saarland University) · doi:10.22028/d291-38109 · Cited by 10 · see citing works
Post diversity: A new lens of social media WOM
Dong, X., Zhang, Z., Zhang, Y., Ao, X., & Tang, T. (2023) · Journal of Business Research · doi:10.1016/j.jbusres.2023.114329 · Cited by 9 · see citing works
Karayianni, C., & Psaltis, C. (2022) · Online Information Review · doi:10.1108/oir-03-2022-0161 · Cited by 8 · see citing works
Pierri, F. (2024) · EPJ Data Science · doi:10.1140/epjds/s13688-024-00501-1 · Cited by 9 · see citing works
Peng, R. X., & Wang, R. Y. (2023) · Online Information Review · doi:10.1108/oir-03-2022-0186 · Cited by 7 · see citing works
Chueri, J., & Törnberg, P. (2024) · Political Research Exchange · doi:10.1080/2474736x.2023.2299121 · Cited by 7 · see citing works
Understanding the Dynamics between Vaping and Cannabis Legalization Using Twitter Opinions
Adhikari, S., Uppal, A., Mermelstein, R., Berger-Wolf, T., & Zheleva, E. (2021) · International Conference on Web and Social Media · doi:10.1609/icwsm.v15i1.18037 · Cited by 9 · see citing works
Retweet Prediction Using Context-Aware Coupled Matrix-Tensor Factorization
Jiang, B., Feng, Y., Wu, J., & Lü, Z. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-29551-6_17 · Cited by 7 · see citing works
Studying Politicians' Information Sharing on Social Media
Meier, F., & Elsweiler, D. (2019) · Conference on Human Information Interaction and Retrieval · doi:10.1145/3295750.3298944 · Cited by 7 · see citing works
Affective and interactional polarization align across countriesPreprint
Falkenberg, M., Zollo, F., Quattrociocchi, W., Pfeffer, J., & Baronchelli, A. (2023) · doi:10.31234/osf.io/ngsb6 · Cited by 11 · see citing works
Tweeting for peace: an analysis of twitter use in Colombia's peace process
Quinchía-Saavedra, C., García, J. A. C., & Pino, J. M. R. (2025) · International Review on Public and Nonprofit Marketing · doi:10.1007/s12208-025-00433-0 · Cited by 6 · see citing works
Twitter communities are associated with changing user’s opinion towards COVID-19 vaccine in Japan
Wu, Q., Sano, Y., Hideki Takayasu, C., Havlin, S., & Takayasu, M. (2025) · Scientific Reports · doi:10.1038/s41598-025-95595-6 · Cited by 6 · see citing works
Characterizing reticulation in online social networks during disasters
Fan, C. H., Shen, J., Mostafavi, A., & Hu, X. (2020) · Applied Network Science · doi:10.1007/s41109-020-00271-5 · Cited by 6 · see citing works
Dijital Çağda Dijital Pandemi: Türkiyede COVID-19 Gündemi
YILDIRIM, A. (2020) · Intermedia International e-journal · doi:10.21645/intermedia.2020.87 · Cited by 6 · see citing works
Elite versus mass polarization on the Brazilian impeachment proceedings of 2016
Moreira, R., de Melo, P. O. S. V., & Pappa, G. (2020) · Social Network Analysis and Mining · doi:10.1007/s13278-020-00706-y · Cited by 4 · see citing works
Friction Interventions to Curb the Spread of Misinformation on Social MediaPreprint
Jahn, L., Rendsvig, R. K., Flammini, A., Menczer, F., & Hendricks, V. F. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2307.11498 · Cited by 6 · see citing works
Retweeting Twitter Hate Speech After Musk Acquisition
Auten, T., & Matta, J. (2024) · Studies in computational intelligence · doi:10.1007/978-3-031-53503-1_22 · Cited by 5 · see citing works
van Vliet, L., Chueri, J., Törnberg, P., & Uitermark, J. (2023) · Party Politics · doi:10.1177/13540688231158486 · Cited by 5 · see citing works
Don't Feed the Troll: Detecting Troll Behavior via Inverse Reinforcement LearningPreprint
Luceri, L., Giordano, S., & Ferrara, E. (2020) · arXiv (Cornell University) · Cited by 5 · see citing works
An ideological asymmetry in the diffusion of moralized content among political elites
Brady, W. J., Van Bavel, J. J., Jost, J., & Wills, J. A. (2018) · doi:10.31234/osf.io/43n5e · Cited by 4 · see citing works
Luo, K. (2025) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2025.117581 · Cited by 4 · see citing works
Morselli, D., Durrheim, K. L., Beramendi, M., Sepahpour‐Fard, M., Mafunda, M. C., Torres, A. M., Ohamadike, N., Quayle, M., & Schuld, M. (2025) · European Journal of Social Psychology · doi:10.1002/ejsp.70038 · Cited by 5 · see citing works
Svozil, M., Gronow, A., & Ocelík, P. (2025) · Environmental Communication · doi:10.1080/17524032.2025.2456230 · Cited by 4 · see citing works
Predicting, evaluating, and explaining top misinformation spreaders via archetypal user behavior
Verdolotti, E., Luceri, L., & Giordano, S. (2025) · Online Social Networks and Media · doi:10.1016/j.osnem.2025.100336 · Cited by 4 · see citing works
In-party love spreads more efficiently than out-party hate in online communities
Martín-Gutíerrez, S., Robles, J. M., Torcal, M., Losada, J. C., & Benito, R. M. (2024) · Scientific Reports · doi:10.1038/s41598-024-65688-9 · Cited by 7 · see citing works
Nip, J. Y. M., & Cheng, Y. (2022) · Journalism and Media · doi:10.3390/journalmedia3030039 · Cited by 4 · see citing works
The Unintended Consequences of Amplifying the Radical Right on Twitter
Fernandes, J. M., & Won, M. (2023) · Political Communication · doi:10.1080/10584609.2023.2232752 · Cited by 5 · see citing works
Li, K., Lv, G., Wang, Z., Liu, Q., Chen, E., & Qiao, L. (2018) · Frontiers of Computer Science · doi:10.1007/s11704-018-7453-x · Cited by 4 · see citing works
Thomas, R., McDowell-Naylor, D., & Cushion, S. (2022) · Journalism Practice · doi:10.1080/17512786.2022.2050469 · Cited by 4 · see citing works
When Groups Fall Apart: Identifying Transnational Polarization with Twitter from the Arab UprisingsPreprint
Kubinec, R., & Owen, J. M. (2018) · doi:10.31235/osf.io/wykmj · Cited by 4 · see citing works
Friction Against Fiction: Adding 'Grit' to Boost Psychological Inoculation Against MisinformationPreprint
Fendt, M., Holford, D. L., & Lewandowsky, S. (2024) · doi:10.31234/osf.io/dzbm7 · Cited by 4 · see citing works
Wang, Z., Lu, Y., Zhou, Y., & Ji, J. (2024) · Journal of Informetrics · doi:10.1016/j.joi.2024.101562 · Cited by 3 · see citing works
A perspective on friction interventions to curb the spread of misinformation
Jahn, L., Rendsvig, R. K., Flammini, A., Menczer, F., & Hendricks, V. F. (2025) · npj Complexity · doi:10.1038/s44260-025-00051-1 · Cited by 3 · see citing works
Arora, M., Kim, H. J., Masuoka, N., & Stout, C. T. (2024) · Political Communication · doi:10.1080/10584609.2024.2377977 · Cited by 3 · see citing works
When Groups Fall Apart: Identifying Transnational Polarization During the Arab Uprisings
Kubinec, R., & Owen, J. M. (2021) · Political Analysis · doi:10.1017/pan.2020.46 · Cited by 3 · see citing works
Sharing Photos on Social Media: Visual Attention Affects Real-World Decision Making
Fagan, S. E., Wade, L., Hugenberg, K., Kapadia, A. C., & Bertenthal, B. I. (2021) · Lecture notes in networks and systems · doi:10.1007/978-3-030-79997-7_25 · Cited by 3 · see citing works
- Authors
- Metaxas, P., Mustafaraj, E., Wong, K., Zeng, L., O'Keefe, M., & Finn, S.
- Affiliations
- Wellesley College (US)
- Venue
- Proceedings of the International AAAI Conference on Web and Social Media
- Published
- 2021-08-03
- Volume
- 9
- Issue
- 1
- Pages
- 658-661
- ISSN
- 2162-3449 (ISSN-L)
- 2334-0770
- OpenAlex
- W2399902206
- Cited by
- 162
- References
- 2
- Citations by year
- 2026: 8 · 2025: 22 · 2024: 27 · 2023: 20 · 2022: 15 · 2021: 15 · 2020: 18 · 2019: 12 · 2018: 14 · 2017: 5 · 2016: 5 · 2015: 1
- FWCI
- 28.38
- Type
- conference paper
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- Proceedings of the International AAAI Conference on Web and Social Media (publishedVersion)
- AAAI Publications (The Association for the Advancement of Artificial Intelligence (AAAI)) (submittedVersion)
- Funders
- National Science Foundation (1117693)
- Topics
- Social Sciences › Communication › Social Media and Politics
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Physical Sciences › Computer Science › Computer Networks and Communications › Media and Digital Communication
- Keywords
- retweets, Twitter, user motivation, hashtags, political hashtags, journalists, user profiles
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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