Effect of Segregation on Opinion Formation in Scale-Free Social Networks: An Agent-based Approach

Document Type : Original Article

Authors

1 ICT Research Institute, Tehran, Iran

2 Department of Electrical and Computer Engineering, University of Tehran, Tehran, Iran

Abstract

We consider the effect of segregation on opinion formation in social networks with and without influential leaders in scale-free random networks, which is found in many social and natural phenomena. We have used agent-based modeling and simulation, focusing on the social impact model of opinion formation. Two simulation scenarios of this opinion formation model have been considered: (1) the original scenario which randomly assigns persuasion strengths to the agents, and (2) a centrality-based scenario, which assigns persuasion strengths proportional to the agents’ centralities. In the latter scenario, hubs are considered more influential leaders who are more connected to others and have higher persuasion strengths than others. The simulation results show a correlation between segregation and change of population opinion in the original model, but no correlation between both variables in the centrality-based scenario. The results lead us to conclude that with strong influential leaders in society, the effect of segregation in opinion formation is neglectable.

Keywords

References

1.     Iacopini, I., Petri, G., Barrat, A. and Latora, V., "Simplicial models of social contagion", Nature Communications, Vol. 10, No. 1, (2019), 1-9, doi: 10.1038/s41467-019-10431-6.
2.     Hołyst, J.A., Kacperski, K. and Schweitzer, F., "Social impact models of opinion dynamics", Annual Reviews of Computational physics, Vol. 9, (2001), 253-273, doi: 10.1142/9789812811578_0005.
3.     Mohammadi, A., and Hamidi, H., "Analysis and evaluation of privacy protection behavior and information disclosure concerns in online social networks", International Journal of Engineering, Transaction B: Applications, Vol. 31, No. 8, (2018), 1234-1239, doi: 10.5829/ije.2018.31.08b.11.
4.     Mansouri, A., Taghiyareh, F. and Hatami, J., "Post-based prediction of users' opinions employing the social impact model improved by emotion", International Journal of Web Research, Vol. 1, No. 2, (2018), 34-42, doi: 10.22133/IJWR.2018.91425.
5.     Srividya, K., Mariyababu, K. and A. M. Sowjanya, "Mining interesting aspects of a product using aspect-based opinion mining from product reviews (research note)", International Journal of Engineering, Transaction B: Applications, Vol. 30, No. 11, (2017), 1707-1713, doi: 10.5829/ije.2017.30.11b.11.
6.     Howard, P.N., Duffy, A., Freelon, D., Hussain, M.M., Mari, W. and Maziad, M. Opening closed regimes: What was the role of social media during the Arab spring? Project on Information Technology and Political Islam  2011; available at SSRN: https://ssrn.com/abstract=2595096,     doi: 10.2139/ssrn.2595096.
7.     Allcott, H. and Gentzkow, M., "Social media and fake news in the 2016 election", Journal of Economic Perspectives, Vol. 31, No. 2, (2017), 211-236, doi: 10.1257/jep.31.2.211.
8.     Narayanan, V., Howard, P.N., Kollanyi, B. and Elswah, M., "Russian involvement and junk news during brexit" (2017), Retrieved from comprop.oii.ox.ac.uk/wp-content/uploads/sites/93/2017/12/Russia-and-Brexit-v27. pdf on the 2nd of November 2020.
9.     Latané, B., "The psychology of social impact", American Psychologist, Vol. 36, No. 4, (1981), 343-356, doi: 10.1037/0003-066X.36.4.343.
10.   Bojanowski, M. and Corten, R., "Measuring segregation in social networks", Social Networks, Vol. 39, (2014), 14-32, doi: 10.1016/j.socnet.2014.04.001.
11.   Feliciani, T., Flache, A. and Tolsma, J., "How, when and where can spatial segregation induce opinion polarization? Two competing models", Vol. 20, No. 2, (2017), 6, doi: 10.18564/jasss.3419.
12.   Mansouri, A. and Taghiyareh, F., "Phase transition in the social impact model of opinion formation in scale-free networks: The social power effect", Journal of Artificial Societies and Social Simulation, Vol. 23, No. 2, (2020), 3, doi: 10.18564/jasss.4232.
13.   Mansouri, A. and Taghiyareh, F., "Correlation of segregation and social networks' majority opinion in the social impact model", in 6th International Conference on Web Research (ICWR), IEEE, 66-71, (2020), doi: 10.1109/ICWR49608.2020.9122279.
14.   Mansouri, A. and Taghiyareh, F., "Effect of segregation on the dynamics of noise-free social impact model of opinion formation through agent-based modeling", International Journal of Web Research, Vol. 2, No. 2, (2019), 36-44, doi: 10.22133/IJWR.2020.226249.1054.
15.   Ndlela, M.N., Social media algorithms, bots and elections in africa, in Social media and elections in africa, volume 1. 2020, Springer.13-37, doi: 10.1007/978-3-030-30553-6_2.
16.   Zhan, M., Liang, H., Kou, G., Dong, Y. and Yu, S., "Impact of social network structures on uncertain opinion formation", IEEE Transactions on Computational Social Systems, Vol. 6, No. 4, (2019), 670-679, doi: 10.1109/TCSS.2019.2916918.
17.   Castellano, C., Fortunato, S. and Loreto, V., "Statistical physics of social dynamics", Reviews of Modern Physics, Vol. 81, No. 2, (2009), 591, doi: 10.1103/RevModPhys.81.591.
18.   Murase, Y., Jo, H.-H., Török, J., Kertész, J. and Kaski, K., "Structural transition in social networks: The role of homophily", Scientific Reports, Vol. 9, No. 1, (2019), 1-8, doi: 10.1038/s41598-019-40990-z.
19.   Salehi, S. M. M. and Pouyan, A. A., "Detecting overlapping communities in social networks using deep learning", International Journal of Engineering, Transaction C: Aspects, Vol. 33, No. 3, (2020), 366-376, doi: 10.5829/IJE.2020.33.03C.01.
20.   ElTayeby, O., Molnar, P. and George, R., "Measuring the influence of mass media on opinion segregation through Twitter", Procedia Computer Science, Vol. 36, (2014), 152-159, doi: 10.1016/j.procs.2014.09.062.
21.   Fershtman, M., "Cohesive group detection in a social network by the segregation matrix index", Social Networks, Vol. 19, No. 3, (1997), 193-207, doi: 10.1016/S0378-8733(96)00295-X.
22.   Rogers, E.M. and Cartano, D.G., "Methods of measuring opinion leadership", Public Opinion Quarterly, Vol. 26, No. 3, (1962), 435-441, doi: 10.1086/267118.
23.   Katz, E., "The two-step flow of communication: An up-to-date report on a hypothesis", Public Opinion Quarterly, Vol. 21, No. 1, (1957), 61-78, doi: 10.1086/266687.
24.   DeMarzo, P.M., Vayanos, D. and Zwiebel, J., "Persuasion bias, social influence, and unidimensional opinions", The Quarterly Journal of Economics, Vol. 118, No. 3, (2003), 909-968, doi: 10.1162/00335530360698469.
25.   Weimann, G., Tustin, D.H., Van Vuuren, D. and Joubert, J., "Looking for opinion leaders: Traditional vs. Modern measures in traditional societies", International Journal of Public Opinion Research, Vol. 19, No. 2, (2007), 173-190, doi: 10.1093/ijpor/edm005.
26.   Baym, N.K., Tune in, log on: Soaps, fandom, and online community. Thousand Oaks, CA: Sage, Vol. 3,  2000.
27.   Riquelme, F., Gonzalez-Cantergiani, P., Hans, D., Villarroel, R. and Munoz, R., "Identifying opinion leaders on social networks through milestones definition", IEEE Access, Vol. 7, (2019), 75670-75677, doi: 10.1109/ACCESS.2019.2922155.
28.   Cha, M., Benevenuto, F., Haddadi, H. and Gummadi, K., "The world of connections and information flow in Twitter", IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, Vol. 42, No. 4, (2012), 991-998, doi: 10.1109/TSMCA.2012.2183359.
29.   Hinz, O., Skiera, B., Barrot, C. and Becker, J.U., "Seeding strategies for viral marketing: An empirical comparison", Journal of Marketing, Vol. 75, No. 6, (2011), 55-71, doi: 10.1509/jm.10.0088.
30.   Iyengar, R., Van den Bulte, C. and Valente, T.W., "Opinion leadership and social contagion in new product diffusion", Marketing Science, Vol. 30, No. 2, (2011), 195-212, doi: 10.1287/mksc.1100.0566.
31.   Chattoe-Brown, E., "Why sociology should use agent-based modelling", Sociological Research Online, Vol. 18, No. 3, (2013), 1-11, doi: 10.5153/sro.3055.
32.   Bianchi, F. and Squazzoni, F., "Agent‐based models in sociology", Wiley Interdisciplinary Reviews: Computational Statistics, Vol. 7, No. 4, (2015), 284-306, doi: 10.1002/wics.1356.
33.   Hauke, J., Lorscheid, I. and Meyer, M., "Recent development of social simulation as reflected in jasss between 2008 and 2014: A citation and co-citation analysis", Journal of Artificial Societies and Social Simulation, Vol. 20, No. 1, (2017), doi: 10.18564/jasss.3238.
34.   Stumpf, M.P. and Porter, M.A., "Critical truths about power laws", Science, Vol. 335, No. 6069, (2012), 665-666, doi: 10.1126/science.1216142.
35.   Broido, A.D. and Clauset, A., "Scale-free networks are rare", Nature Communications, Vol. 10, No. 1, (2019), 1-10, doi: 10.1038/s41467-019-08746-5.
36.   Barabási, A.-L. and Albert, R., "Emergence of scaling in random networks", Science, Vol. 286, No. 5439, (1999), 509-512, doi: 10.1126/science.286.5439.509.
37.   Bianconi, G. and Marsili, M., "Number of cliques in random scale-free network ensembles", Physica D: Nonlinear Phenomena, Vol. 224, No. 1-2, (2006), 1-6, doi: 10.1016/j.physd.2006.09.013.
38.   Morris, A.D. and Staggenborg, S., "Leadership in social movements", The Blackwell companion to social movements, 171-196, Malden, MA: Blackwell, 2004.
39.   Gao, J., Schoenebeck, G. and Yu, F.-Y., "The volatility of weak ties: Co-evolution of selection and influence in social networks", in Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems, 619-627, (2019).
40.   Bakshy, E., Messing, S. and Adamic, L.A., "Exposure to ideologically diverse news and opinion on Facebook", Science, Vol. 348, No. 6239, (2015), 1130-1132, doi: 10.1126/science.aaa1160.
41.   Luceri, L., Giordano, S. and Ferrara, E., "Detecting troll behavior via inverse reinforcement learning: A case study of Russian trolls in the 2016 us election", in Proceedings of the International AAAI Conference on Web and Social Media, (2020), 417-427.
International Journal of Engineering
Volume 34, Issue 1
TRANSACTIONS A: Basics
January 2021
Pages 66-74

Files

Share

How to cite

Statistics