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Information gerrymandering and undemocratic decisions
Abstract
People must integrate disparate sources of information when making decisions, especially in social contexts. But information does not always flow freely. It can be constrained by social networks 1-3 and distorted by zealots and automated bots 4. Here we develop a voter game as a model system to study information flow in collective decisions. Players are assigned to competing groups (parties) and placed on an 'influence network' that determines whose voting intentions each player can observe. Players are incentivized to vote according to partisan interest, but also to coordinate their vote with the entire group. Our mathematical analysis uncovers a phenomenon that we call information gerrymandering: the structure of the influence network can sway the vote outcome towards one party, even when both parties have equal sizes and each player has the same influence. A small number of zealots, when strategically placed on the influence network, can also induce information gerrymandering and thereby bias vote outcomes. We confirm the predicted effects of information gerrymandering in social network experiments with n = 2,520 human subjects. Furthermore, we identify extensive information gerrymandering in real-world influence networks, including online political discussions leading up to the US federal elections, and in historical patterns of bill co-sponsorship in the US Congress and European legislatures. Our analysis provides an account of the vulnerabilities of collective decision-making to systematic distortion by restricted information flow. Our analysis also highlights a group-level social dilemma: information gerrymandering can enable one party to sway decisions in its favour, but when multiple parties engage in gerrymandering the group loses its ability to reach consensus and remains trapped in deadlock.
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“Recent work suggests that network structural effects can lead to “information gerrymandering” that induces undemocratic outcomes whereby a majority of the electorate votes against the electorate’s interest”
From Stewardship of global collective behavior (2021) · cites this paper as [97]
In the citing paper
“even a small number of agents with aggressive attitudes can have a substantial influence on the majority view, a process called “information gerrymandering” (66).”
From Troll and divide: the language of online polarization (2022) · cites this paper as [66]
In the citing paper
“Exposure to polarizing attitudes even produced by a small number of agents can have a devastating effect on political compromise in a social network”
From Troll and divide: the language of online polarization (2022) · cites this paper as [66]
Citations
Cites
Hardin, G. (1968) · Science · doi:10.1126/science.162.3859.1243 · Cited by 25,167
The spread of true and false news online
Vosoughi, S., Roy, D., & Aral, S. (2018) · Science · doi:10.1126/science.aap9559 · Cited by 8,847
Lazer, D. M. J., Baum, M. A., Benkler, Y., Berinsky, A. J., Greenhill, K. M., Menczer, F., Metzger, M. J., Nyhan, B., Pennycook, G., Rothschild, D., et al. (2018) · Science · doi:10.1126/science.aao2998 · Cited by 4,160
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
Iyengar, S., Sood, G., & Lelkes, Y. (2012) · Public Opinion Quarterly · doi:10.1093/poq/nfs038 · Cited by 2,996
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
A 61-million-person experiment in social influence and political mobilization
Bond, R. M., Fariss, C. J., Jones, J. J., Kramer, A. D. I., Marlow, C., Settle, J. E., & Fowler, J. H. (2012) · Nature · doi:10.1038/nature11421 · Cited by 2,575
The spreading of misinformation online
Del Vicario, M., Bessi, A., Zollo, F., Petroni, F., Scala, A., Caldarelli, G., Stanley, H. E., & Quattrociocchi, W. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1517441113 · Cited by 2,407
Pennycook, G., & Rand, D. G. (2019) · Cognition · doi:10.1016/j.cognition.2018.06.011 · Cited by 2,022
Exposure to opposing views on social media can increase political polarization
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
Research works printed but not linked: 1
- Stephanopoulos, N. O. & McGhee, E. Partisan gerrymandering and the efficiency gap. Univ. Chic. Law Rev. 82, 831–900 (2015).
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Jusup, M., Holme, P., Kanazawa, K., Takayasu, M., Romić, I., Wang, Z., Geček, S., Lipić, T., Boris Podobnik, A., Wang, L., et al. (2022) · Physics Reports · doi:10.1016/j.physrep.2021.10.005 · Cited by 688
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Social influence and unfollowing accelerate the emergence of echo chambers
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
Adaptive social networks promote the wisdom of crowds
Almaatouq, A., Noriega-Campero, A., Alotaibi, A., Krafft, P. M., Moussaïd, M., & Pentland, A. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1917687117 · Cited by 185
“For instance, we speculate that network structure distortions to individuals’ local observations—such as information gerrymandering (57) and majority illusion (58)—may discourage the participants from searching for peers with high signal quality in a dynamic network with limited feedback.”
BackgroundCognitive reflection correlates with behavior on Twitter
Mosleh, M., Pennycook, G., Arechar, A. A., & Rand, D. G. (2021) · Nature Communications · doi:10.1038/s41467-020-20043-0 · Cited by 175
Shared partisanship dramatically increases social tie formation in a Twitter field experiment
Mosleh, M., Martel, C., Eckles, D., & Rand, D. G. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2022761118 · Cited by 123
“In line with this self-reported dislike for counter-partisans, observational studies find that Americans are substantially more likely to have face-to-face social interactions with copartisans (3), and to be connected to co-partisans on social media networks (4) – all of which may contribute to “echo chambers” where l…”
BackgroundSelf-citationMosleh, M., Martel, C., Eckles, D., & Rand, D. (2021) · Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445642 · Cited by 106
“Accordingly, there is growing concern about potential negative impacts of social media on public discourse [2].”
BackgroundTahmasbi, F., Schild, L., Ling, C., Blackburn, J., Stringhini, G., Zhang, Y., & Zannettou, S. (2021) · International World Wide Web Conference (WWW) · doi:10.1145/3442381.3450024 · Cited by 212
Schild, L., Ling, C., Blackburn, J., Stringhini, G., Zhang, Y., & Zannettou, S. (2020) · MPG.PuRe (Max Planck Society) · Cited by 89
Troll and divide: the language of online polarization
Simchon, A., Brady, W. J., & Van Bavel, J. J. (2022) · PNAS Nexus · doi:10.1093/pnasnexus/pgac019 · Cited by 85
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“Strategically placed zealot bots can in fact bias voting outcomes and win elections 125 .”
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Ulrich, Y., Kawakatsu, M., Tokita, C. K., Saragosti, J., Chandra, V., Tarnita, C. E., & Kronauer, D. J. C. (2021) · PLoS Biology · doi:10.1371/journal.pbio.3001269 · Cited by 55
“In turn, these patterns affect important processes such as collective decision-making [5], the transmission and evolution of disease [91,92], and the evolution of cooperative behavior [93,94].”
BackgroundNetwork Engineering Using Autonomous Agents Increases Cooperation in Human Groups
Shirado, H., & Christakis, N. A. (2020) · iScience · doi:10.1016/j.isci.2020.101438 · Cited by 50
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 57
Strategies for integrating disparate social information
Molleman, L., Tump, A. N., Gradassi, A., Herzog, S. M., Jayles, B., Kurvers, R. H. J. M., & van den Bos, W. (2020) · Proceedings of the Royal Society B Biological Sciences · doi:10.1098/rspb.2020.2413 · Cited by 35
“The way in which individuals integrate personal and social information shapes a wide range of collective phenomena, including the spread of knowledge across social networks (1), the development of financial markets (2), political mobilization (3) and voting outcomes (4).”
BackgroundSegregation and clustering of preferences erode socially beneficial coordination
de Vasconcelos, V. V. L., Constantino, S. M., Dannenberg, A., Lumkowsky, M., Weber, E. U., & Levin, S. A. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2102153118 · Cited by 43
van Baar, J. M., & FeldmanHall, O. (2022) · American Psychologist · doi:10.1037/amp0000814 · Cited by 38
“In this case, if one can convince voters of one party that the opposing party will most likely win, this would motivate people to vote for the opposing party out of a “realist” view that gridlock can be avoided (Stewart et al., 2019).”
BackgroundInfluentialChadwick, A. (2019) · Figshare · Cited by 35
Lin, T.-H., Chang, M.-C., Chang, C.-C., & Chou, Y.-H. (2022) · Social Science & Medicine · doi:10.1016/j.socscimed.2022.114744 · Cited by 36
A guide to similarity measures and their data science applications
Levy, A., Shalom, B. R., & Chalamish, M. (2025) · Journal Of Big Data · doi:10.1186/s40537-025-01227-1 · Cited by 33
Inequality, identity, and partisanship: How redistribution can stem the tide of mass polarization
Stewart, A. J., Plotkin, J. B., & McCarty, N. M. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2102140118 · Cited by 40
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Jacobs, A. Z., & Watts, D. J. (2021) · Management Science · doi:10.1287/mnsc.2021.3997 · Cited by 30
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Atkinson, Q. D., & Jacquet, J. (2021) · Perspectives on Psychological Science · doi:10.1177/17456916211018454 · Cited by 35
“…et al., 2014; Markowitz & Shariff, 2012), media (Eveland & Cooper, 2013; Feldman et al., 2012), and online networks (Guilbeault et al., 2018; Stewart et al., 2019) influence climate change discourse; how culture trumps the individual conscience (Cohn et al., 2014); the role of…”
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“Finally, the alignment of views and behavior through social norms can also have adverse effects when this happens within segregated groups in a polarized society (Stewart et al. 2019; Green et al. 2020; Bühren and Dannenberg 2021; Druckman et al. 2021; Vasconcelos et al. 2021).”
BackgroundInformation gerrymandering in social networks skews collective decision-making
Bergstrom, C. T., & Bak-Coleman, J. B. (2019) · Nature · doi:10.1038/d41586-019-02562-z · Cited by 30
Tang, X., Zhang, L., & Yuan, K. (2024) · Journal of Destination Marketing & Management · doi:10.1016/j.jdmm.2024.100859 · Cited by 30
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Jones, M. I., Sirianni, A., & Fu, F. (2022) · Humanities and Social Sciences Communications · doi:10.1057/s41599-022-01056-0 · Cited by 34
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van Baar, J. M., Nassar, M. R., Deng, W., & FeldmanHall, O. (2021) · Nature Human Behaviour · doi:10.1038/s41562-021-01207-4 · Cited by 26
“The strategy–mini-block interaction was significant (F(1,347) = 16.”
BackgroundFake News in Social NetworksPreprint
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Liu, L., Wang, X., Chen, X., Tang, S., & Zheng, Z. (2021) · Frontiers in Physics · doi:10.3389/fphy.2021.649852 · Cited by 23
“It is sometimes strategically utilized by partisan organizations to obtain more votes in an election, for instance, deploying vast social bots [13] and information gerrymandering [14].”
BackgroundEKGTF: A knowledge-enhanced model for optimizing social network-based meteorological briefings
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de Bruin, D., van Baar, J. M., Rodríguez, P. L., & FeldmanHall, O. (2023) · Science Advances · doi:10.1126/sciadv.abq5920 · Cited by 25
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Liu, H., Wang, X., Liu, L., & Li, Z. (2021) · Frontiers in Physics · doi:10.3389/fphy.2021.658130 · Cited by 19
Pescetelli, N., Barkoczi, D., & Cebrián, M. (2022) · Applied Network Science · doi:10.1007/s41109-022-00488-6 · Cited by 22
“The bot is represented as a stubborn agent that does not change its opinion but sticks to the same opinion throughout the simulation (Stewart et al. 2019; Karan et al. 2018; Yildiz et al. 2013).”
BackgroundQuantifying the vulnerabilities of the online public square to adversarial manipulation tactics
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Bartal, A., Pliskin, N., & Tsur, O. (2020) · PLoS ONE · doi:10.1371/journal.pone.0230811 · Cited by 17
“However, the social network plays a fundamental role in the dynamics of spreading [53, 57, 58].”
BackgroundDuijf, H. (2021) · Synthese · doi:10.1007/s11229-021-03203-7 · Cited by 16
“, 2017) and information exchange (O’Connor and Weatherall, 2019; Stewart et al., 2019) have been used to study the spread of false and true beliefs in different social circumstances.”
BackgroundTargeted Allocation of Marketing Resource in Networks Based on Opinion Dynamics
Lang, N., Wang, L., & Zha, Q. (2022) · Mathematics · doi:10.3390/math10030394 · Cited by 15
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Chen, H., Yang, C. H., Zhang, X., Liu, Z., Sun, M., & Jin, J. (2021) · Journal of Social Computing · doi:10.23919/jsc.2021.0011 · Cited by 19
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Wong, Y. N., Jones, R., Das, R., & Jackson, P. J. B. (2023) · Big Data & Society · doi:10.1177/20539517231184892 · Cited by 15
Ellaky, Z., & Benabbou, F. (2024) · Scientific African · doi:10.1016/j.sciaf.2024.e02269 · Cited by 16
Mapping illegal wildlife trade networks provides new opportunities for conservation actions
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“…understanding of how information about novel information (Aplin et al., 2015) and imminent threats (Rosenthal, Twomey, Hartnett, Wu, & Couzin, 2015) spread through animal social networks, and how social structure can lead to undemocratic outcomes in human social networks (Stewart et al., 2019).”
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Jiang, Y., Mi, Q., & Zhu, L. (2023) · Nature Neuroscience · doi:10.1038/s41593-023-01258-y · Cited by 16
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“, stewardship [53] and social networks [54].”
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Etta, G., Galeazzi, A., Hutchings, J. R., Smith, C. S. J., Conti, M., Quattrociocchi, W., & Riva, G. V. D. (2022) · PLoS ONE · doi:10.1371/journal.pone.0267022 · Cited by 15
Adversarial attacks on voter model dynamics in complex networks
Chiyomaru, K., & Takemoto, K. (2022) · Physical review. E · doi:10.1103/physreve.106.014301 · Cited by 12
“A previous study [11] demonstrated that information gerrymandering, i.”
InfluentialCombatir las noticias falsas. El perfil profesional del verificador de la información en España
Ruiz, M. J. U., Rubio, L. M. C., & Verdú, F. J. M. (2022) · Vivat Academia · doi:10.15178/va.2022.155.e1365 · Cited by 12
Fitting in and breaking up: A nonlinear version of coevolving voter models
Kureh, Y. H., & Porter, M. A. (2020) · Physical review. E · doi:10.1103/physreve.101.062303 · Cited by 12
Terradillos, J. O., Morales-i-Gras, J., & Ureta, A. L. (2020) · Revista Mediterránea de Comunicación · doi:10.14198/medcom2020.11.2.2 · Cited by 12
Matchanova, A., Woods, S. P., Neighbors, C., Medina, L. D., Podell, K., Beltran‐Najera, I., Alex, C., Babicz, M. A., & Thompson, J. L. (2023) · Current Psychology · doi:10.1007/s12144-023-04464-w · Cited by 13
“In addition to the phenomenon of echo chambers and filter bubbles (Bakshy et al., 2015; Stewart et al., 2019), social media platforms may discourage people from reflecting on accuracy (Goldhaber, 1997).”
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Lang, N., Zha, Q., & Wang, L. (2023) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2023.3270876 · Cited by 10
“Stewart et al. [42] devoted a section to analyze this phenomenon in election campaigns, wherein the network structure was found to bias the vote outcome toward one of the parties.”
BackgroundDigital health—high tech or high touch?
Gangl, C., & Krychtiuk, K. A. (2023) · Wiener Medizinische Wochenschrift · doi:10.1007/s10354-022-00991-6 · Cited by 11
Simchon, A., Turkin, C., Svoray, T., Kloog, I., Dorman, M., & Gilead, M. (2021) · Journal of Experimental Social Psychology · doi:10.1016/j.jesp.2021.104221 · Cited by 10
Opinion formation with zealots on temporal network
Luo, Y., Cheng, C. C. L., Li, Y., & Yu, C. B. (2021) · Communications in Nonlinear Science and Numerical Simulation · doi:10.1016/j.cnsns.2021.105772 · Cited by 9
Stewart, A. J., Arechar, A. A., Rand, D. G., & Plotkin, J. B. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2315195121 · Cited by 16
Multilayer social reinforcement induces bistability on multiplex networks
Liu, L., Wang, X., Tang, S., Zheng, H., & Zheng, Z. (2021) · Journal of Statistical Mechanics Theory and Experiment · doi:10.1088/1742-5468/abffcb · Cited by 8
How does homophily shape the topology of a dynamic network?
Li, X., Mobilia, M., Rucklidge, A. M., & Zia, R. K. P. (2021) · Physical review. E · doi:10.1103/physreve.104.044311 · Cited by 10
Zadelaar, J. N., van Rentergem, J. A. A., Schaaf, J. V., Dekkers, T. J., de Vent, N. R., Dekkers, L. M. S., Olthof, M. C., Jansen, B. R. J., & Huizenga, H. M. (2021) · Judgment and Decision Making · doi:10.1017/s1930297500008482 · Cited by 12
“…the effects of internal and external information use in the population is relevant to a variety of real-world settings such as political party voting (Stewart et al., 2019), court jury rulings (Levett & Devine, 2017; Kovera et al., 1997; Moore & Gump, 1995), economic drifts and marketing tactics…”
BackgroundA model-independent theory of consensus and dissensus decision makingPreprint
Franci, A., Golubitsky, M., Bizyaeva, A., & Ehrich, N. (2019) · arXiv (Cornell University) · doi:10.13140/rg.2.2.19757.92646 · Cited by 15
Richardson, E., & Keil, F. C. (2022) · Scientific Reports · doi:10.1038/s41598-021-04680-z · Cited by 8
Zhang, H. (2022) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2022.112760 · Cited by 7
The Strengths and Weaknesses of Crowds to Address Global Problems
Broomell, S. B., & Davis‐Stober, C. P. (2023) · Perspectives on Psychological Science · doi:10.1177/17456916231179152 · Cited by 7
“These subcrowds and how they influence the perceptions and actions of individuals can be quite complex, especially on social media and other digital platforms—see recent work on “information gerrymandering” (Bergstrom & Bak-Coleman, 2019; Stewart et al., 2019) and the network models thereof.”
BackgroundRepulsion in controversial debate drives public opinion into fifty-fifty stalemate
Krause, S. M., Weyhausen-Brinkmann, F., & Bornholdt, S. (2019) · Physical review. E · doi:10.1103/physreve.100.042307 · Cited by 7
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Altoe, F., Moreira, C., Pinto, H. S., & Jorge, J. A. (2024) · Human Behavior and Emerging Technologies · doi:10.1155/2024/1069670 · Cited by 13
“The formed new sub-groups can lead to a phenomenon known as information gerrymandering [160], where STRO individuals can keep negatively held opinions alive, even if nonfactual, as demonstrated in [96].”
BackgroundNot all bridges connect: integration in multi-community networks
Heydari, B., Heydari, P., & Mosleh, M. (2019) · Journal of Mathematical Sociology · doi:10.1080/0022250x.2019.1694519 · Cited by 7
Bolinches, R. T., & Alonso, N. (2020) · RiuNet (Universitat Politècnica de València) · Cited by 7
Gaisbauer, F., Strandburg‐Peshkin, A., & Giese, H. (2022) · Social Networks · doi:10.1016/j.socnet.2022.01.013 · Cited by 6
“A “pure inertia” strategy captures that forgoing a group-wide consensus is preferred over giving up one’s preference (Stewart et al., 2019).”
BackgroundResultInfluentialBaptista, J. P., Gradim, A., & Correia, E. (2022) · Observatorio (OBS*) · doi:10.15847/obsobs16320222082 · Cited by 9
Change My Mind: Data Driven Estimate of Open-Mindedness from Political Discussions
Pansanella, V., Morini, V., Squartini, T., & Rossetti, G. (2023) · Studies in computational intelligence · doi:10.1007/978-3-031-21127-0_8 · Cited by 6
“With respect to modelling works, simulation results have been compared to election data [34], survey data [30], social experiments data [31], or social media data [36] in order to manually tune parameters or learn them with automated approaches [18,28,13,33].”
MethodIdentifying and Eliminating Majority Illusion in Social Networks
Grandi, U., Kanesh, L., Lisowski, G., Sridharan, R., & Turrini, P. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i4.25634 · Cited by 6
Zinchenko, V. V., Boichenko, M., Slyusarenko, O., Popovych, M., Радченко, Л., Iehupov, M. V., & Базелюк, В. В. (2023) · E3S Web of Conferences · doi:10.1051/e3sconf/202341902001 · Cited by 6
How does homophily shape the topology of a dynamic network?
Li, X., Mobilia, M., Rucklidge, A., & Zia, R. (2021) · White Rose Research Online (University of Leeds, The University of Sheffield, University of York) · Cited by 6
Lack of evidence for correlation between COVID-19 infodemic and vaccine acceptancePreprint
Valensise, C. M., Cinelli, M., Nadini, M., Galeazzi, A., Peruzzi, A., Etta, G., Zollo, F., Baronchelli, A., & Quattrociocchi, W. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2107.07946 · Cited by 18
Mosleh, M., Allen, J. N. L., & Rand, D. G. (2025) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2425739122 · Cited by 7
“This finding provides evidence for the classic “echo chamber” hypothesis on social media ( 26 – 29 ).”
A brief natural history of misinformation
Kong, L.-W., Gallart, L., Grassick, A. G., Love, J. W., Nayak, A., & Hein, A. M. (2025) · Journal of The Royal Society Interface · doi:10.1098/rsif.2025.0161 · Cited by 5
Neurocomputations of strategic behavior: From iterated to novel interactions
Jiang, Y., Wu, H., Mi, Q., & Zhu, L. (2022) · Wiley Interdisciplinary Reviews Cognitive Science · doi:10.1002/wcs.1598 · Cited by 7
“Strategic behavior has been the subject of intense study in game theory (Camerer, 2011; Osborne & Rubinstein, 1994), evolutionary biology (Nowak, 2006; Weibull, 1997), sociology (Easley & Kleinberg, 2010; Moulin, 1986), political science (Farquharson, 1969; Stewart et al., 2019), cognitive and decision neuroscience (G…”
BackgroundLavorgna, A., & Corda, A. (2024) · International Criminology · doi:10.1007/s43576-024-00134-4 · Cited by 8
“This antagonism constructed and amplified within social media environments has major political (e.g., ‘information gerryman-dering’, see Stewart et al., 2019) and social (e.g., erosion of empathy linked to the marginalization of outgroups, see Wojcieszak, 2010; the increased use of confrontation,…”
Social dilemma for 30 years: Progress, framework, and future based on CiteSpace analysis
Gao, J., Geng, Y., Jiang, X., Li, J., & Yan, Y. (2024) · Medicine · doi:10.1097/md.0000000000041138 · Cited by 8
Kirwan, R., Mallett, G. S., Ellis, L. R., & Flanagan, A. (2022) · Current Developments in Nutrition · doi:10.1093/cdn/nzac037 · Cited by 5
“While efficient, recruiting participants from such groups may increase the likelihood of information gerrymandering, whereby a small number of zealots may influence others’ biased survey response outcomes (10).”
Liu, W., Zhu, J., Лю, П., Wang, P., & Song, W. Q. (2023) · Group Decision and Negotiation · doi:10.1007/s10726-023-09851-z · Cited by 5
“The group must integrate disparate opinions when making decisions (Stewart et al. 2019).”
BackgroundWhere next for behavioral public policy?
Chater, N., & Loewenstein, G. (2023) · Behavioral and Brain Sciences · doi:10.1017/s0140525x23002091 · Cited by 4
Predicting Voting Outcomes for Multi-Alternative Elections in Social Networks
Liu, X., Ren, F., Su, G., Zhang, M., Gu, W., & Kato, S. (2024) · IEEE Access · doi:10.1109/access.2024.3425160 · Cited by 5
“According to findings in [1], an alternative with a positive influence gap can sway election results by influencing members who support the other alternative.”
BackgroundMethodResultInfluentialSocially 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
“Studies investigating the spiral-of-silence effect in social media posit that people assess the prevailing public opinion based on authentic content from credible media sources (Fox & Holt, 2018; Gearhart & Zhang, 2013; Kushin et al., 2019; Stoycheff, 2016).”
BackgroundResultPredicting voting outcomes in the presence of communities, echo chambers and multiple parties
Bara, J., Lev, O., & Turrini, P. (2022) · Artificial Intelligence · doi:10.1016/j.artint.2022.103773 · Cited by 5
Ognibene, D., Donabauer, G., Theophilou, E., Buršić, S., Lomonaco, F., Wilkens, R., Hernández‐Leo, D., & Kruschwitz, U. (2023) · Datenbank-Spektrum · doi:10.1007/s13222-023-00436-3 · Cited by 6
Social networks affect redistribution decisions and polarization
Tsvetkova, M., Olsson, H., & Galešić, M. (2025) · PNAS Nexus · doi:10.1093/pnasnexus/pgaf339 · Cited by 4
Detecting science-based health disinformation: a stylometric machine learning approach
Williams, J. A., AlEroud, A. F., & Zimmerman, D. (2023) · Journal of Computational Social Science · doi:10.1007/s42001-023-00213-y · Cited by 4
“Other research areas, such as disin-formation modeling and dynamics [52, 53], social computing and health disinforma-tion [53, 54], crowd-source mediation [55], and crisis-related misinformation [56], could potentially benefit from an IQ-based approach.”
BackgroundSocial botnets and the challenges of cyber situation awareness
Yadav, S. (2024) · AI and Ethics · doi:10.1007/s43681-024-00530-6 · Cited by 5
“Much of these online psychological operations consist of 5–10% socialbots that tie together various small networks and alter the natural assortment of influence [18, 73, 83].”
Propagation structure feature of entertainment news in the Weibo online social network
Zhao, Z. (2021) · Europhysics Letters (EPL) · doi:10.1209/0295-5075/135/16002 · Cited by 4
Metadata
- Authors
- Stewart, A. J., Mosleh, M., Diakonova, M., Arechar, A. A., Rand, D. G., & Plotkin, J. B.
- Affiliations
- University of Houston (US)
- Massachusetts Institute of Technology (US)
- University of Oxford (GB)
- Centro de Investigación y Docencia Económicas (MX)
- University of Pennsylvania (US)
- Corresponding author
- Stewart, Alexander J.
- ORCID
- Venue
- Nature
- Published
- 2019-09-04
- Volume
- 573
- Issue
- 7772
- Pages
- 117-121
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- OpenAlex
- W2972275627
- Cited by
- 176
- References
- 29
- Citations by year
- 2026: 5 · 2025: 14 · 2024: 18 · 2023: 31 · 2022: 32 · 2021: 48 · 2020: 22 · 2019: 6
- FWCI
- 12.49
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Nature (publishedVersion)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Topics
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Opinion Dynamics and Social Influence
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Game Theory and Voting Systems
- Keywords
- group decision making, influence networks, Zealots, bots, social dilemmas, democratic legitimacy, information flow, political polarization, online political discussion, United States Congress
- MeSH terms
- Decision Making, Democracy, Game Theory, Group Processes, Humans, Models, Theoretical, Politics, Truth Disclosure, Bias, Knowledge, Social Networking, Social Media
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