print

Attitudes toward authorities as a factor of the political trolling perception on social media in Russia

Stukal D.K.,

HSE University, Moscow, Russia, denis.stukal@gmail.com

elibrary_id: 1096522 | ORCID: 0000-0001-6240-5714 | RESEARCHER_ID: ABA-3314-2020

Shilina A.N.,

HSE University, Moscow, Russia, ashilina@hse.ru

elibrary_id: 1123153 | ORCID: 0000-0002-6667-0686 |

Article received: 2021.09.24. Accepted: 2022.05.23
DOI: 10.17976/jpps/2022.04.14

Rubric: Russia today

For citation:

Stukal D.K., Shilina A.N. Attitudes toward authorities as a factor of the political trolling perception on social media in Russia. – Polis. Political Studies. 2022. No. 4. https://doi.org/10.17976/jpps/2022.04.14

This research was conducted with the support of the Russian Science Foundation grant No. 21-78-00079, https://rscf.ru/project/21-78-00079.

Abstract

Political trolling has recently become a new manipulation tool employed in digital politics. A large body of political science research that addresses online trolling focuses however either on troll detection or the description of trolls’ activities and strategies. This literature however largely ignores the issue of the perception of trolls by social media users. As a result, scarce research sheds light on the effects of political trolling on social media. From a methodological perspective, the situation is exacerbated by the prevalence of studies that draw on troll detection via human annotation of social media accounts. Sidestepping the issue of troll perception can dramatically bias the results of empirical studies in this context. This paper helps bridge this gap in academic literature on political trolling by analyzing the perception of trolling on VK, one of most popular social media platforms in Russia. We draw on scholarship on selective perception to develop the hypothesis that people with different attitudes towards the Russian government will tend to label social media posts with the opposite stance as trolling. We test this hypothesis using an original collection of survey data that we analyze using state-of-the-art mixed effects regression models. Our analysis reveals that oppositionleaning respondents show differential perception of pro-government and anti-government posts and tend to label the former as trolling. However, we do not find similar results of pro-government social media users. Our findings indicate important methodological limitations of empirical studies based on human-labeled accounts and highlight starking differences in the perception of the online political information by governmentleaning and opposition-leaning respondents, thereby raising important questions for future research. 

Keywords

trolls, internet trolls, social media, social networks, political communication, selective perception, online communities.

 Full Text

Angrist, J.D., & Pischke, J.-S. (2009). Mostly harmless econometrics: an empiricist’s companion. Princeton: Princeton University Press. 392 p.

Bail, C.A., Guay, B., Maloney, E., Combs, A., Hillygus, D.S., Merhout, F., Freelon, D., & Volfovsky, A. (2020). Assessing the Russian internet research agency’s impact on the political attitudes and behaviors of American Twitter users in late 2017. Proceedings of the national academy of sciences, 117(1), 243-250. https://doi.org/10.1073/pnas.1906420116

Ball-Rokeach, S.J. (1998). A Theory of media power and a theory of media use: different stories, questions, and ways of thinking. Mass Communication and Society, 1(1-2), 5-40. https://doi.org/10.1080/15205436.1998.9676398

Cheng, J., Danescu-Niculescu-Mizil, C., & Leskovec, J. (2015). Antisocial behavior in online discussion communities. arXiv:1504.00680 (arXiv:1504.00680v2). https://doi.org/10.48550/arXiv.1504.00680

Chun, S.A., Holowczak, R., Dharan, K.N., Wang, R., Basu, S., & Geller, J. (2019). Detecting political bias trolls in Twitter data. Proceedings of the 15th International Conference on Web Information Systems and Technologies (WEBIST 2019), 334-342. Vienna, Austria. https://doi.org/10.5220/0008350303340342

Dlala, I.O., Attiaoui, D., Martin, A., & Yaghlane, B.B. (2015). Trolls Identification within an uncertain framework. arXiv:1501.05272 (arXiv: 1501.05272v1). https://doi.org/10.48550/arXiv.1501.05272

Golovchenko, Y., Buntain, C., Eady, G., Brown, M.A., & Tucker, J.A. (2020). Cross-platform state propaganda: Russian trolls on Twitter and YouTube during the 2016 U.S. presidential election. The International Journal of Press/Politics, 25(3), 357-389. https://doi.org/10.1177/1940161220912682

Gomila, R. (2021). Logistic or linear? Estimating causal effects of experimental treatments on binary outcomes using regression analysis. Journal of Experimental Psychology: General, 150(4), 700-709. https://doi.org/10.1037/xge0000920

Gunitsky, S. (2015). Corrupting the cyber-commons: social media as a tool of autocratic stability. Perspectives on Politics, 13(1), 42-54. https://doi.org/10.1017/S1537592714003120

Herring, S., Job-Sluder, K., Scheckler, R., & Barab, S. (2002). Searching for safety online: managing “trolling” in a feminist forum. The Information Society, 18(5), 371-384. https://doi.org/10.1080/01972240290108186

Howard, Ph.N., Woolley, S., & Calo, R. (2018). Algorithms, bots, and political communication in the US 2016 election: The challenge of automated political communication for election law and administration. Journal of Information Technology & Politics, 15(2), 81-93. https://doi.org/10.1080/19331681.2018.1448735

Keller, F.B., Schoch, D., Stier, S., Yang, JH. (2020). Political astroturfing on Twitter: how to coordinate a disinformation campaign. Political Communication, 37(2), 256-280. https://doi.org/10.1080/10584609.2019.1661888

King, G., Pan, J., & Roberts, M.E. (2017). How the Chinese government fabricates social media posts for strategic distraction, not engaged argument. American Political Science Review, 111(3), 484-501. https://doi.org/10.1017/S0003055417000144

Knobloch-Westerwick, S., Mothes, C., & Polavin, N. (2020). Confirmation bias, ingroup bias, and negativity bias in selective exposure to political information. Communication Research, 47(1), 104-124. https://doi.org/10.1177/0093650217719596

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., Schudson, M., Sloman, S.A., Sunstein, C.R., Thorson, E.A., Watts, D.J., & Zittrain, J.L. (2018). The science of fake news. Science, 359(6380), 1094-1096. https://doi.org/10.1126/science.aao2998

Levy, M.R., & Windahl, S. (1984). Audience activity and gratifications: a conceptual clarification and exploration. Communication Research, 11(1), 51-78. https://doi.org/10.1177/009365084011001003

Linvill, D.L., Boatwright, B.C., Grant, W.J., & Warren, P.L. (2019). “The Russians are hacking my brain!” investigating Russia’s internet research agency twitter tactics during the 2016 United States presidential campaign. Computers in Human Behavior, 99, 292-300. https://doi.org/10.1016/j.chb.2019.05.027

Lukito, J. (2020). Coordinating a multi-platform disinformation campaign: internet research agency activity on three U.S. social media platforms, 2015 to 2017. Political Communication, 37(2), 238-255. https://doi.org/10.1080/10584609.2019.1661889

Miller, B. (2016). Automated detection of Chinese government astroturfers using network and social metadata. SSRN 2738325. Haven Hall, Ann Arbor: Department of Statistics, University of Michigan. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2738325

Pearson, G.D.H., & Knobloch-Westerwick, S. (2019). Is the confirmation bias bubble larger online? Pre-election confirmation bias in selective exposure to online versus print political information. Mass Communication and Society, 22(4), 466-486. https://doi.org/10.1080/15205436.2019.1599956

Shin, J. (2008). Morality and internet behavior: a study of the internet troll and its relation with morality on the Internet. In K. McFerrin, R. Weber, R. Carlsen & D. Willis (Ed.), Proceedings of SITE 2008—Society for Information Technology & Teacher Education International Conference (pp. 2834-2840). Las Vegas, NV: Association for the Advancement of Computing in Education (AACE). URL: https://www.learntechlib.org/noaccesspresent/27652/46654/ (accessed 07.06.2022).

Stroud, N.J., Muddiman, A., & Lee, J.K. (2014). Seeing media as group members: an evaluation of partisan bias perceptions. Journal of Communication, 64(5), 874-894. https://doi.org/10.1111/jcom.12110

Tomaiuolo, M., Lombardo, G., Mordonini, M., Cagnoni, S., & Poggi, A. (2020). A survey on troll detection. Future Internet, 12(2), 31-44. https://doi.org/10.3390/fi12020031

Umeozor, S.N. (2020). Information retrieval: a communication process in the 21st century library. International Journal of Knowledge Content Development & Technology, 10(2), 7-18. http://dx.doi.org/10.5865/IJKCT.2020.10.2.007

Wang, T., & Merkle, E.C. (2018). merDeriv: derivative computations for linear mixed effects models with application to robust standard errors. Journal of Statistical Software, Code Snippets, 87(1), 1-16. https://doi.org/10.18637/jss.v087.c01

Wheeler, N.E., Allidina, S., Long, E.U., Schneider, S.P., Haas, I.J., & Cunningham, W.A. (2020). Ideology and predictive processing: coordination, bias, and polarization in socially constrained error minimization. Current Opinion in Behavioral Sciences, 34, 192-198. https://doi.org/10.1016/j.cobeha.2020.05.002

Wooldridge, J.M. (2010). Econometric analysis of cross section and panel data. Cambridge, MA; London: The MIT press. 1096 p.

Brodovskaya, E.V., Dombrovskaya, A.Yu., Karzubov, D.N., & Sinyakov, A.V. (2017). Developing methodology for “smart” search for political process markers in social media. Monitoring of Public Opinion: Economic and Social Changes, 5, 79-104. (In Russ.) https://doi.org/10.14515/monitoring.2017.5.06

Chugrov, S.V. (2021). The strategic empathy in the world politics: is it possible to overcome the Rashomon effect and fundamental attribution error? Polis. Political Studies, 5, 109-126. (In Russ.) https://doi.org/10.17976/jpps/2021.05.08

Glukhova, A.V. (2016). The role of provocation in policy. Vlast’ (The Authority), 24(3), 92-96. (In Russ.)

Evdokimov, V.A. (2019). Trolling in media sphere. The Science of Person: Humanitarian Researches, 38(4), 41-47. (In Russ.) https://doi.org/10.17238/issn1998-5320.2019.38.41

Martyanov, D.S. (Ed.). (2019). Upravlyaemost’ i diskurs virtual’nykh soobshchestv v usloviyakh politiki postpravdy [Governance and discourse of virtual communities in the context of post-truth politics]. St. Petersburg: Eleksis. 312 p. (In Russ.)

Popova, O.V., & Suslov, S.I. (2021). Network analysis of political internet communities: from formalized to “unobserved” groups. Political Science (Ru), 1, 160-182. (In Russ.) http://www.doi.org/10.31249/poln/2021.01.07 

See also: