BOTS LEADING THE PEOPLE? MODEL OF SOCIAL BOT'S IMPACT ON POLITICAL MOBILIZATION AND DEMOBILIZATION IN SOCIAL MEDIA
DOI:
https://doi.org/10.17072/2218-1067-2021-4-16-29Keywords:
bots; social media; political communication; agent-based model; protest; political mobilization; political demobilization; computational modellingAbstract
Bots (automated accounts) in social media have gained more attention from social scientists in recent years. The former are employed by both governments and civil society groups in order to manipulate online political discussion in social media. Nevertheless, there is no conclusive evidence on their effectiveness as a tool to bolster (mobilize) or suppress (demobilize) political discussion in social media. This paper presents a novel agent-based model, capable of simulating bot deployment as both mobilization and demobilization tool. Results of the simulations reveal three major effects of bot deployment. First, bots are more effective at demobilizing opponents than mobilizing supporters. Second, transmitting radical opinions via bots may backfire, demobilizing a certain group rather than mobilizing it. Third, the effectiveness of social bots is dependent on homophily: more homophilous networks are less susceptible to bots’ influence. Results of modeling may both advance our estimations of bots’ effectiveness and serve as a tool to generate potential hypotheses for future empirical research.References
Ахременко, А. С., Петров, А.П., Пхоун, Ч. and Жеглов, С. А. (2021) ‘Как информаци-онно-коммуникационные технологии меняют тренды в моделировании поли-тических процессов: к агентному под-ходу’, Политическая наука, (1), сс. 12–45. DOI: http://www.doi.org/10.31249/poln/2021.01.01. [Akhremenko, A. S., Pe-trov, A. P., Phoun Ch., Zheglov, S.A. (2021) ‘How information and communi-cation technologies change trends in modelling political processes: towards an agent-based approach’ [‘Kak infor-macionno-kommunikacionnye tehnologii menjajut trendy v modelirovanii politich-eskih processov: k agentnomu podhodu’], Political science (RU), (1), pp. 12–45. DOI: http://www.doi.org/10.31249/poln/2021.01.01 (In Russ.)].
Assenmacher, D., Clever, L., Frischlich, L., Quandt, T., Trautmann, H. and Grimme, C. (2020) ‘Demystifying social bots: On the intelligence of automated social me-dia actors’, Social Media+ Society, 6(3), pp. 1‒16. DOI: https://doi.org/10.1177/2056305120939264.
Barberá, P. (2014) How social media reduces mass political polarization. Evidence from Germany, Spain, and the US [Online]. Avaible at: URL: http://pablobarbera.com/staic/barbera_polarization_APSA.pdf (5th November 2021).
Benigni, M. C., Joseph, K. and Carley, K. M. (2019) ‘Bot-ivistm: assessing information manipulation in social media using net-work analytics’, in: Emerging Research Challenges and Opportunities in Compu-tational Social Network Analysis and Min-ing, pp. 19–42. Springer, Cham. DOI: https://doi.org/10.1007/978-3-319-94105-9_2.
Beskow, D. M. and Carley, K. M. (2019) ‘Agent based simulation of bot disinformation maneuvers in Twitter’, in: 2019 Winter simulation conference (WSC), pp. 750–761. IEEE. DOI:10.1109/WSC40007.2019.9004942.
Bond, R. M. and Sweitzer, M. D. (2018) ‘Politi-cal homophily in a large-scale online communication network’, Communica-tion Research, pp. 1–23. DOI: https://doi.org/10.1177/0093650218813655.
Brachten, F., Stieglitz, S., Hofeditz, L., Kloppen-borg, K. and Reimann, A. (2017) ‘Strate-gies and Influence of Social Bots in a 2017 German state election-A case study on Twitter’ [online], [Preprint]. [Viewed date 05 September 2021]. URL: https://arXiv:1710.07562.
Carley, K. M. (2020) ‘Social cybersecurity: an emerging science’, Computational and mathematical organization theory, 26(4), pp. 365‒381. DOI: https://doi.org/10.1007/s10588-020-09322-9.
Chen, W., Pacheco, D., Yang, K. C. and Menczer, F. (2021) ‘Neutral bots probe political bias on social media’, Nature communications, 12(1), pp. 1–10. DOI: https://doi.org/10.1038/s41467-021-25738-6.
Della Porta, D. and Diani, M. (2006) Social movements: An introduction. John Wiley & Sons.
DellaPosta, D., Shi, Y. and Macy, M. (2015) ‘Why do liberals drink lattes?’, American Journal of Sociology, 120(5), pp. 1473–1511. DOI: https://doi.org/10.1086/681254.
Diamond, L. (2010) ‘Liberation technology’, Journal of democracy, 21(3), pp. 69–83. DOI:10.1353/jod.0.0190.
Farrell, H. (2012) ‘The consequences of the in-ternet for politics’, Annual review of polit-ical science, 15, pp. 35–52. DOI: https://doi.org/10.1146/annurev-polisci-030810-110815.
Ferrara, E. (2017) ‘Disinformation and social bot operations in the run up to the 2017 French presidential election’ [online], [Preprint]. [Viewed date 05 September 2021]. URL: https://arXiv:1707.00086.
Ferrara, E., Varol, O., Davis, C., Menczer, F. and Flammini, A. (2016) ‘The rise of social bots’, Communications of the ACM, 59(7), pp. 96‒104. DOI: https://doi.org/10.1145/2818718.
González-Bailón, S. and Wang, N. (2016) ‘Net-worked discontent: The anatomy of pro-test campaigns in social media’, Social networks, 44, pp. 95–104. DOI: https://doi.org/10.1016/j.socnet.2015.07.003.
Gould, R. V., (1991) ‘Multiple networks and mo-bilization in the Paris Commune, 1871’, American Sociological Review, 56(6), pp. 716‒729. DOI: https://doi.org/10.2307/2096251.
Huber, G. A. and Malhotra, N. (2017) ‘Political homophily in social relationships: Evi-dence from online dating behavior’, The Journal of Politics, 79(1), pp. 269–283. DOI: https://doi.org/10.1086/687533.
Keller, F. B., Schoch, D., Stier, S. and Yang, J. (2020) ‘Political astroturfing on Twitter: How to coordinate a disinformation cam-paign’, Political Communication, 37(2), pp. 256‒280. DOI: https://doi.org/10.1080/10584609.2019.1661888.
King, G., Pan, J. and Roberts, M. E. (2017) ‘How the Chinese government fabricates social media posts for strategic distraction, not engaged argument’, American political science review, 111(3), pp. 484–501. DOI: https://doi.org/10.1017/S0003055417000144.
Klandermans, B. (1984) ‘Mobilization and partic-ipation: Social-psychological expansisons of resource mobilization theory’, Ameri-can sociological review, pp. 583–600. DOI: https://doi.org/10.2307/2095417.
Kuran, T. (1991) ‘Now out of never: The element of surprise in the East European revolu-tion of 1989’, World politics, 44(1), pp. 7‒48. DOI: https://doi.org/10.2307/2010422.
Linvill, D. L. and Warren, P. L. (2020) ‘Troll fac-tories: Manufacturing specialized disin-formation on Twitter’, Political Commu-nication, 37(4), pp. 447–467. DOI: https://doi.org/10.1080/10584609.2020.1718257.
Marx, G. T., (1974) ‘Thoughts on a neglected category of social movement participant: The agent provocateur and the inform-ant’, American Journal of Sociology, 80(2), pp. 402‒442. DOI: https://doi.org/10.1086/225807.
McPherson, M., Smith-Lovin, L. and Cook, J. M. (2001) ‘Birds of a feather: Homophily in social networks’, Annual review of sociol-ogy, 27(1), pp. 415–444. DOI: https://doi.org/10.1146/annurev.soc.27.1.415.
Metaxas, P. T. and Mustafaraj, E. (2012) ‘Social media and the elections’, Science, 338(6106), pp. 472–473. DOI: 10.1126/science.1230456.
Mosleh, M., Martel, C., Eckles, D. and Rand, D. G. (2021) ‘Shared partisanship dramati-cally increases social tie formation in a Twitter field experiment’, Proceedings of the National Academy of Sciences, 118(7). DOI: https://doi.org/10.1073/pnas.2022761118.
Noelle‐Neumann, E. (1974) ‘The spiral of silence a theory of public opinion’, Journal of communication, 24(2), pp. 43–51. DOI: https://doi.org/10.1111/j.1460-2466.1974.tb00367.x.
Ratkiewicz, J., Conover, M., Meiss, M., Gon-çalves, B., Patil, S., Flammini, A. and Menczer, F. (2011) ‘Truthy: mapping the spread of astroturf in microblog streams’, in: Proceedings of the 20th international conference companion on World wide web, pp. 249‒252. DOI: https://doi.org/10.1145/1963192.1963301.
Ross, B., Pilz, L., Cabrera, B., Brachten, F., Neubaum, G. and Stieglitz, S. (2019) ‘Are social bots a real threat? An agent-based model of the spiral of silence to an-alyse the impact of manipulative actors in social networks’, European Journal of Information Systems, 28(4), pp. 394–412. DOI: https://doi.org/10.1080/0960085X.2018.1560920.
Sanovich, S., Stukal, D. and Tucker, J. A. (2018) ‘Turning the virtual tables: Government strategies for addressing online opposi-tion with an application to Russia’, Com-parative Politics, 50(3), pp. 435–482. DOI: 10.5129/001041518822704890.
Schäfer, F., Evert, S. and Heinrich, P. (2017) ‘Japan's 2014 General Election: Political Bots, Right-Wing Internet Activism, and Prime Minister Shinzō Abe's Hidden Na-tionalist Agenda’, Big data, 5(4), pp. 294–309. DOI: 10.1089/big.2017.0049.
Shin, J. and Thorson, K. (2017) ‘Partisan selec-tive sharing: The biased diffusion of fact-checking messages on social media’, Journal of Communication, 67(2), pp. 233‒255. DOI: https://doi.org/10.1111/jcom.12284.
Siegel, D. A. (2009) ‘Social networks and collec-tive action’, American journal of political science, 53(1), pp. 122–138. DOI: https://doi.org/10.1111/j.1540-5907.2008.00361.x.
Siegel, D. A. (2018) ‘Analyzing computational models’, American Journal of Political Science, 62(3), pp. 745–759. DOI: https://doi.org/10.1111/ajps.12364.
Silva, B. C. and Proksch, S. O. (2021) ‘Fake It ‘Til You Make It: A Natural Experiment to Identify European Politicians’ Benefit from Twitter Bots’, American Political Science Review, 115(1), pp. 316–322. DOI: https://doi.org/10.1017/S0003055420000817.
Sobolev, A. (2019) How social media reduces mass political polarization. Evidence from Germany, Spain, and the US [Online]. Availble at: URL: https: //www.wpsanet.org/papers/docs/2019W-Feb-Anton-Sobolev-Trolls-VA.pdf (5th November 2021).
Sohn, D. and Geidner, N. (2016) ‘Collective dy-namics of the spiral of silence: The role of ego-network size’, International Jour-nal of Public Opinion Research, 28(1), pp. 25‒45. DOI: https://doi.org/10.1093/ijpor/edv005.
Sohn, D. (2019) ‘Spiral of silence in the social media era: A simulation approach to the interplay between social networks and mass media’, Communication Research, pp. 1‒28. DOI: https://doi.org/10.1177/0093650219856510.
Stein, E. A. (2017) ‘Are ICTs Democratizing Dic-tatorships? New Media and Mass Mobili-zation’, Social Science Quarterly, 98(3), pp. 914–941. DOI: 10.1111/ssqu.12439.
Stella, M., Ferrara, E. and De Domenico, M. (2018) ‘Bots increase exposure to nega-tive and inflammatory content in online social systems’, Proceedings of the Na-tional Academy of Sciences, 115(49), pp. 12435‒12440. DOI: 10.1073/pnas.1803470115.
Stukal, D., Sanovich, S., Bonneau, R. and Tuck-er, J. A. (2017) ‘Detecting bots on Rus-sian political Twitter’, Big data, 5(4), pp. 310–324. DOI: 10.1089/big.2017.0038.
Stukal, D., Sanovich, S., Tucker, J. A. and Bon-neau, R. (2019) ‘For Whom the Bot Tolls: A Neural Networks Approach to Measuring Political Orientation of Twit-ter Bots in Russia’, SAGE Open, 9(2), pp. 1–16. DOI: https://doi.org/10.1177/2158244019827715.
Suárez-Serrato, P., Roberts, M. E., Davis, C. and Menczer, F. (2016) ‘November. On the influence of social bots in online pro-tests’, in: International Conference on So-cial Informatics, pp. 269–278. Springer, Cham. DOI: http://dx.doi.org/10.1007/978-3-319-47874-6_19.
Valenzuela, S., Arriagada, A. and Scherman, A. (2014) ‘Facebook, Twitter, and youth en-gagement: A quasi-experimental study of social media use and protest behavior us-ing propensity score matching’, Interna-tional Journal of Communication, 8, pp. 2046–2070.
Verkamp, J. P. and Gupta, M. (2013) ‘Five inci-dents, one theme: Twitter spam as a weapon to drown voices of protest’, in: 3rd {USENIX} Workshop on Free and Open Communications on the Internet ({FOCI} 13).
Walter, N. and Murphy, S. T. (2018) ‘How to unring the bell: A meta-analytic approach to correction of misinformation’, Com-munication Monographs, 85(3), pp. 423–441. DOI: https://doi.org/10.1080/03637751.2018.1467564.
Wang, G., Mohanlal, M., Wilson, C., Wang, X., Metzger, M., Zheng, H. and Zhao, B. Y. (2012) ‘Social turing tests: Crowdsourc-ing sybil detection’ [online], [Preprint]. [Viewed date 05 September 2021]. URL: https://arxiv.org/abs/1205.3856.
Weeks, B. E. (2015) ‘Emotions, partisanship, and misperceptions: How anger and anxiety moderate the effect of partisan bias on susceptibility to political misinfor-mation’, Journal of communication, 65(4), pp. 699‒719. DOI: https://doi.org/10.1111/jcom.12164.
Wilensky, U. (1999) NetLogo [online]. Center for Connected Learning and Computer-Based Modeling, Northwestern Universi-ty, Evanston, IL. [Viewed date 05 Sep-tember 2021]. URL: http://ccl.northwestern.edu/netlogo/.
Zerback, T., Töpfl, F. and Knöpfle, M. (2021) ‘The disconcerting potential of online disinformation: Persuasive effects of as-troturfing comments and three strategies for inoculation against them’, New Media & Society, 23(5), pp. 1080–1098. DOI: https://doi.org/10.1177/1461444820908530.
Downloads
Published
How to Cite
Issue
Section
License
The author grants the Publisher of the journal (Perm State University) the right to use their article in the journal, as well as to include the text of the abstract, the full text of the article and information about authors in the "Russian Science Citation Index" (RSCI).
The author agrees to the processing of personal data.
The right to use the journal as a whole belongs to the Publisher and acts indefinitely on the territory of the Russian Federation and beyond in accordance with cl. 1260 of the Russian Federation Civil Code.
There is no author's fee paid for providing the above rights by the author.
The author of the article included in the journal retains the exclusive right to it, regardless of the Publisher's right to use the journal as a whole:
a. The authors retain their copyrights to the article and transfer the right of the first publication along with the article to the journal, while also licensing it on the terms of the Creative Commons Attribution License, which allows others to distribute this article with the obligatory indication of authorship of the article and reference to the original publication in this journal.
b. The authors retain the right to enter into separate, additional contractual agreements for the non-exclusive dissemination of the version of the text published by this journal (for example, post it in a university archive or publish it in a book), with reference to the original publication in this journal.
c. d. Authors are allowed to post their text on the Internet (for example, in a university archive or on their personal website) before and during the review process by this journal, as this can lead to a fruitful discussion and to higher number of the references to this published work (Please refer to The Effect of Open Access).
Submission of an article by the author implies that they agree for it to be used by the Publisher on the above conditions and to be included in the RSCI system. It also implicates that the author is aware of the terms of its use. The information about the author sent to the Publisher, including by e-mail, is also considered as such consent.
The editorial board posts the full text of the article on the Perm State University site: http://www.psu.ru and in the OJS system at http://press.psu.ru
The publication fee is not collected and fees are not paid. The author's copy is sent to the author to the address provided by them.
