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.