Opinion dynamics of online social network users: a micro-level analysis

References

• A. Petrov, O. Proncheva. Identifying the Topics of Russian Political Talk Shows // Proceedings of the Conference on Modeling and Analysis of Complex Systems and Processes 2020, October 22–24 (MACSPro 2020). pp. 79–86. CEUR-WS.org, online. http://ceur-ws.org/Vol-2795/short1.pdf
   Google Scholar
• Abelson, R. P. (1964). Mathematical models of the distribution of attitudes under controversy. Contributions to Mathematical Psychology, 142–160.
   Google Scholar
• Abid, O., Jamoussi, S., & Ayed, Y. B. (2018). Deterministic models for opinion formation through communication: A survey. Online Social Networks and Media, 6, 1–17. https://doi.org/10.1016/j.osnem.2018.02.002
   Google Scholar
• Altafini, C. (2013). Consensus problems on networks with antagonistic interactions. IEEE Transactions on Automatic Control, 58(4), 935–946. https://doi.org/10.1109/TAC.2012.2224251
   Web of Science ®Google Scholar
• Aral, S., & Nicolaides, C. (2017). Exercise contagion in a global social network. Nature Communications, 8(1), 14753. https://doi.org/10.1038/ncomms14753
   PubMed Web of Science ®Google Scholar
• Asch, S. E. (1961). Effects of group pressure upon the modification and distortion of judgments. In Documents of gestalt psychology (pp. 222–236). University of California Press. https://doi.org/10.1525/9780520313514–017
   Google Scholar
• Bakshy, E., Messing, S., & Adamic, L. A. (2015). Exposure to ideologically diverse news and opinion on Facebook. Science, 348(6239), 1130–1132. https://doi.org/10.1126/science.aaa1160
   PubMed Web of Science ®Google Scholar
• Banisch, S., & Olbrich, E. (2019). An Argument Communication Model of Polarization and Ideological Alignment. ArXiv:1809.06134 [Physics]. http://arxiv.org/abs/1809.06134
   Google Scholar
• Barberá, P. (2014). Birds of the same feather tweet together: Bayesian ideal point estimation using Twitter data. Political Analysis, 23(1), 76–91. https://doi.org/10.1093/pan/mpu011
   Web of Science ®Google Scholar
• Bond, R. M., Fariss, C. J., Jones, J. J., Kramer, A. D., Marlow, C., Settle, J. E., & Fowler, J. H. (2012). A 61-million-person experiment in social influence and political mobilization. Nature, 489(7415), 295. https://doi.org/10.1038/nature11421
   PubMed Web of Science ®Google Scholar
• Böttcher, L., & Gersbach, H. (2020). The great divide: Drivers of polarization in the US public. EPJ Data Science, 9(1), 1–13. https://doi.org/10.1140/epjds/s13688-020-00249-4
   Web of Science ®Google Scholar
• Castellano, C., Fortunato, S., & Loreto, V. (2009). Statistical physics of social dynamics. Reviews of Modern Physics, 81(2), 591. https://doi.org/10.1103/RevModPhys.81.591
   Web of Science ®Google Scholar
• Centola, D. The spread of behavior in an online social network experiment. (2010). Science, 329(5996), 1194–1197. Scopus. https://doi.org/10.1126/science.1185231
   PubMed Web of Science ®Google Scholar
• Centola, D. (2019). Influential networks. Nature Human Behaviour, 3(7), 664–665. https://doi.org/10.1038/s41562-019-0607-5
   PubMed Web of Science ®Google Scholar
• Christakis, N. A., & Fowler, J. H. (2013). Social contagion theory: Examining dynamic social networks and human behavior. Statistics in Medicine, 32(4), 556–577. https://ssrn.com/abstract=2974413 or 
   PubMed Web of Science ®Google Scholar
• Clemm von Hohenberg, Bernhard and Mäs, Michael and Pradelski, Bary, Micro Influence and Macro Dynamics of Opinion Formation (October 29, 2017). Available at SSRN: https://ssrn.com/abstract=2974413 or https://doi.org/10.2139/ssrn.2974413
   Google Scholar
• Dandekar, P., Goel, A., & Lee, D. T. (2013). Biased assimilation, homophily, and the dynamics of polarization. Proceedings of the National Academy of Sciences, 110(15), 5791–5796. https://doi.org/10.1073/pnas.1217220110
   PubMed Web of Science ®Google Scholar
• Deffuant, G., Neau, D., Amblard, F., & Weisbuch, G. (2000). Mixing beliefs among interacting agents. Advances in Complex Systems, 3, 87–98. https://doi.org/10.1142/S0219525900000078
   Google Scholar
• DeGroot, M. H. (1974). Reaching a consensus. Journal of the American Statistical Association, 69(345), 118–121. https://ssrn.com/abstract=2974413 or 
   Web of Science ®Google Scholar
• Del Vicario, M., Scala, A., Caldarelli, G., Stanley, H. E., & Quattrociocchi, W. (2017). Modeling confirmation bias and polarization. Scientific Reports, 7(1), 40391. https://doi.org/10.1038/srep40391
   PubMed Web of Science ®Google Scholar
• Flache, A., & Mäs, M. (2008). How to get the timing right. A computational model of the effects of the timing of contacts on team cohesion in demographically diverse teams. Computational and Mathematical Organization Theory, 14(1), 23–51. https://doi.org/10.1007/s10588-008-9019-1
   Web of Science ®Google Scholar
• Flache, A., Mäs, M., Feliciani, T., Chattoe-Brown, E., Deffuant, G., Huet, S., & Lorenz, J. (2017). Models of Social Influence: Towards the Next Frontiers. Journal of Artificial Societies & Social Simulation, 20(4), 4. https://doi.org/10.18564/jasss.3521
   Web of Science ®Google Scholar
• Frey, D. (1986). Recent research on selective exposure to information. Advances in Experimental Social Psychology, 19, 41–80. https://doi.org/10.1016/S0065-2601(08)60212-9
   Google Scholar
• Friedkin, N. E., & Bullo, F. (2017). How truth wins in opinion dynamics along issue sequences. Proceedings of the National Academy of Sciences, 114(43), 11380–11385. https://doi.org/10.1073/pnas.1710603114
   PubMed Web of Science ®Google Scholar
• Friedkin, N. E., Proskurnikov, A. V., & Bullo, F. (2021). Group dynamics on multidimensional object threat appraisals. Social Networks, 65, 157–167. https://doi.org/10.1016/j.socnet.2020.12.009
   Web of Science ®Google Scholar
• Friedkin, N. E., Proskurnikov, A. V., Tempo, R., & Parsegov, S. E. (2016). Network science on belief system dynamics under logic constraints. Science, 354(6310), 321–326. https://doi.org/10.1126/science.aag2624
   PubMed Web of Science ®Google Scholar
• Gaisbauer, F., Olbrich, E., & Banisch, S. (2020). Dynamics of opinion expression. Physical Review E, 102(4), 042303. https://doi.org/10.1103/PhysRevE.102.042303
   PubMed Web of Science ®Google Scholar
• Geschke, D., Lorenz, J., & Holtz, P. (2019). The triple-filter bubble: Using agent-based modelling to test a meta-theoretical framework for the emergence of filter bubbles and echo chambers. British Journal of Social Psychology, 58(1), 129–149. https://doi.org/10.1111/bjso.12286
   PubMed Web of Science ®Google Scholar
• Hegselmann, R., & Krause, U. (2002). Opinion dynamics and bounded confidence models, analysis, and simulation. Journal of Artificial Societies and Social Simulation, 5(3). https://www.jasss.org/5/3/2.html
   Web of Science ®Google Scholar
• Holme, P., & Newman, M. E. J. (2006). Nonequilibrium phase transition in the coevolution of networks and opinions. Physical Review E, 74(5), 056108. https://doi.org/10.1103/PhysRevE.74.056108
   Web of Science ®Google Scholar
• Kerckhove, C. V., Martin, S., Gend, P., Rentfrow, P. J., Hendrickx, J. M., Blondel, V. D., & Sánchez, A. (2016). Modelling Influence and Opinion Evolution in Online Collective Behaviour. PLOS ONE, 11(6), e0157685. https://doi.org/10.1371/journal.pone.0157685
   PubMed Web of Science ®Google Scholar
• Knippenberg, D. V., Vries, N. D., & Knippenberg, A. V. (1990). Group status, group size and attitude polarization. European Journal of Social Psychology, 20(3), 253–257. https://doi.org/10.1002/ejsp.2420200306
   Web of Science ®Google Scholar
• Kovanen, L., Kaski, K., Kertész, J., & Saramäki, J. (2013). Temporal motifs reveal homophily, gender-specific patterns, and group talk in call sequences. Proceedings of the National Academy of Sciences, 110(45), 18070–18075. https://doi.org/10.1073/pnas.1307941110
   PubMed Web of Science ®Google Scholar
• Kozitsin, I. V. (2020). Formal models of opinion formation and their application to real data: Evidence from online social networks. The Journal of Mathematical Sociology, 1–28. https://doi.org/10.1080/0022250X.2020.1835894
   Web of Science ®Google Scholar
• Kozitsin, I. V., Chkhartishvili, A. G., Marchenko, A. M., Norkin, D. O., Osipov, S. D., Uteshev, I. A., Goiko, V. L., Palkin, R. V., & Myagkov, M. G. (2020). Modeling Political Preferences of Russian Users Exemplified by the Social Network Vkontakte. Mathematical Models and Computer Simulations, 12(2), 185–194. https://doi.org/10.1134/S2070048220020088
   Google Scholar
• Kozyreva, A., Lorenz-Spreen, P., Hertwig, R., Lewandowsky, S., & Herzog, S. (2020). Public attitudes towards algorithmic personalization and use of personal data online: Evidence from Germany, Great Britain, and the US. PsyArXiv. https://doi.org/10.31234/osf.io/3q4mg
   Google Scholar
• Krizan, Z., & Baron, R. S. (2007). Group polarization and choice-dilemmas: How important is self-categorization? European Journal of Social Psychology, 37(1), 191–201. https://doi.org/10.1002/ejsp.345
   Web of Science ®Google Scholar
• Kurahashi-Nakamura, T., Mäs, M., & Lorenz, J. (2016). Robust clustering in generalized bounded confidence models. Journal of Artificial Societies and Social Simulation, 19(4), 4. https://doi.org/10.18564/jasss.3220
   Web of Science ®Google Scholar
• Liu, C. C., & Srivastava, S. B. (2015). Pulling Closer and Moving Apart: Interaction, Identity, and Influence in the U.S. Senate, 1973 to 2009. American Sociological Review, 80(1), 192–217. https://doi.org/10.1177/0003122414564182
   Web of Science ®Google Scholar
• Mäs, M. (2019). Challenges to Simulation Validation in the Social Sciences. A Critical Rationalist Perspective. In C. Beisbart & N. J. Saam (Eds.), Computer Simulation Validation: Fundamental Concepts, Methodological Frameworks, and Philosophical Perspectives (pp. 857–879). Springer International Publishing. https://doi.org/10.1007/978-3-319-70766-2_35
   Google Scholar
• Mäs, M., Flache, A., Helbing, D., & Bergstrom, C. T. (2010). Individualization as driving force of clustering phenomena in humans. PLoS Computational Biology, 6(10), 10. https://doi.org/10.1371/journal.pcbi.1000959
   Web of Science ®Google Scholar
• Mäs, M., Flache, A., & Perc, M. (2013). Differentiation without distancing. Explaining bi-polarization of opinions without negative influence. PloS One, 8(11), e74516. https://doi.org/10.1371/journal.pone.0074516
   PubMed Web of Science ®Google Scholar
• Mazen, R., & Leventhal, H. (1972). The influence of communicator-recipient similarity upon the beliefs and behavior of pregnant women. Journal of Experimental Social Psychology, 8(4), 289–302. https://doi.org/10.1016/0022-1031(72)90019-4
   Web of Science ®Google Scholar
• McPherson, M., Smith-Lovin, L., & Cook, J. M. (2001). Birds of a feather: Homophily in social networks. Annual Review of Sociology, 27(1), 415–444. https://doi.org/10.1146/annurev.soc.27.1.415
   Web of Science ®Google Scholar
• Mønsted, B., Sapieżyński, P., Ferrara, E., Lehmann, S., & Lambiotte, R. (2017). Evidence of complex contagion of information in social media: An experiment using Twitter bots. PLOS ONE, 12(9), e0184148. https://doi.org/10.1371/journal.pone.0184148
   PubMed Web of Science ®Google Scholar
• Moussaïd, M., Kämmer, J. E., Analytis, P. P., Neth, H., & Szolnoki, A. (2013). Social Influence and the Collective Dynamics of Opinion Formation. PLOS ONE, 8(11), e78433. https://doi.org/10.1371/journal.pone.0078433
   PubMed Web of Science ®Google Scholar
• Newman, M. E. J. (2001). Clustering and preferential attachment in growing networks. Physical Review E, 64(2), 025102. https://doi.org/10.1103/PhysRevE.64.025102
   Web of Science ®Google Scholar
• Newman, M. E. J. (2018). Networks. Oxford university press.
   Google Scholar
• Nickerson, R. S. (1998). Confirmation bias: A ubiquitous phenomenon in many guises. Review of General Psychology, 2(2), 175. https://doi.org/10.1037/1089-2680.2.2.175
   Google Scholar
• Perra, N., & Rocha, L. E. (2019). Modelling opinion dynamics in the age of algorithmic personalisation. Scientific Reports, 9(1), 1–11. https://doi.org/10.1038/s41598-019-43830-2
   PubMed Web of Science ®Google Scholar
• Peshkovskaya, A., Babkina, T., & Myagkov, M. (2019). Gender effects and cooperation in collective action: A laboratory experiment. Rationality and Society, 31(3), 337–353. https://doi.org/10.1177/1043463119858788
   Web of Science ®Google Scholar
• Proskurnikov, A. V., & Tempo, R. (2017). A tutorial on modeling and analysis of dynamic social networks. Part I. Annual Reviews in Control, 43, 65–79. https://doi.org/10.1016/j.arcontrol.2017.03.002
   Web of Science ®Google Scholar
• Proskurnikov, A. V., & Tempo, R. (2018). A tutorial on modeling and analysis of dynamic social networks. Part II. In Annual Reviews in Control.
   Google Scholar
• Proskurnikov, A. V., Tempo, R., Cao, M., & Friedkin, N. E. (2017). Opinion evolution in time-varying social influence networks with prejudiced agents. IFAC-PapersOnLine, 50(1), 11896–11901. https://doi.org/10.1016/j.ifacol.2017.08.1424
   Google Scholar
• Ravazzi, C., Dabbene, F., Lagoa, C., & Proskurnikov, A. V. (2020). Learning hidden influences in large-scale dynamical social networks: A data-driven sparsity-based approach. ArXiv:2007.06713 [Cs, Eess]. http://arxiv.org/abs/2007.06713
   Google Scholar
• Rossi, W. S., Polderman, J. W., & Frasca, P. (2019). The closed loop between opinion formation and personalised recommendations. ArXiv:1809.04644 [Physics]. http://arxiv.org/abs/1809.04644
   Google Scholar
• Sherif, M., & Hovland, C. I. (1961). Social judgment: Assimilation and contrast effects in communication and attitude change. Yale Univer. Press.
   Google Scholar
• Snijders, T. A. B. (2017). Stochastic Actor-Oriented Models for Network Dynamics. Annual Review of Statistics and Its Application, 4(1), 343–363. https://doi.org/10.1146/annurev-statistics-060116-054035
   Web of Science ®Google Scholar
• Takács, K., Flache, A., Mäs, M., & Amblard, F. (2016). Discrepancy and Disliking Do Not Induce Negative Opinion Shifts. PLOS ONE, 11(6), e0157948. https://doi.org/10.1371/journal.pone.0157948
   PubMed Web of Science ®Google Scholar
• Tang, T., & Chorus, C. G. (2019). Learning Opinions by Observing Actions: Simulation of Opinion Dynamics Using an Action-Opinion Inference Model. Journal of Artificial Societies and Social Simulation, 22(3), 2. https://doi.org/10.18564/jasss.4020
   Web of Science ®Google Scholar
• Ugander, J., Backstrom, L., Marlow, C., & Kleinberg, J. (2012). Structural diversity in social contagion. Proceedings of the National Academy of Sciences, 109(16), 5962–5966. http://ceur-ws.org/Vol-2795/short1.pdf
   PubMed Web of Science ®Google Scholar