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On the Impact of Social Media Recommendations on Opinion Consensus

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AIxIA 2021 – Advances in Artificial Intelligence (AIxIA 2021)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13196))

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Abstract

We consider a discrete opinion formation problem in a setting where agents are influenced by both information diffused by their social relations and from recommendations received directly from the social media manager. We study how the “strength” of the influence of the social media and the homophily ratio affect the probability of the agents of reaching a consensus and how they can determine the type of consensus reached.

In a simple 2-symmetric block model we prove that agents converge either to a consensus or to a persistent disagreement. In particular, we show that when the homophily ratio is large, the social media has a very low capacity of determining the outcome of the opinion dynamics. On the other hand, when the homophily ratio is low, the social media influence can have an important role on the dynamics, either by making harder to reach a consensus or inducing it on extreme opinions.

Finally, in order to extend our analysis to more general and realistic settings we give some experimental evidences that our results still hold on general networks.

Partially supported by GNCS-INdAM and by the Italian MIUR PRIN 2017 Project ALGADIMAR “Algorithms, Games, and Digital Markets”.

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Notes

  1. 1.

    Figures only shows experimental results only for some of the considered networks. Similar results have been obtained for the other network models.

References

  1. Acar, E., Greco, G., Manna, M.: Group reasoning in social environments. In: AAMAS. pp. 1296–1304 (2017)

    Google Scholar 

  2. Allcott, H., Braghieri, L., Eichmeyer, S., Gentzkow, M.: The welfare effects of social media. Am. Econ. Rev. 110(3), 629–76 (2020)

    Article  Google Scholar 

  3. Androniciuc, A.I.: Using social media in political campaigns. Evidence from Romania. SEA-Pract. Appl. Sci. 4(10), 51–57 (2016)

    Google Scholar 

  4. Anunrojwong, J., Candogan, O., Immorlica, N.: Social learning under platform influence: extreme consensus and persistent disagreement. In: SSRN (2020)

    Google Scholar 

  5. Auletta, V., Caragiannis, I., Ferraioli, D., Galdi, C., Persiano, G.: Minority becomes majority in social networks. In: Markakis, E., Schäfer, G. (eds.) WINE 2015. LNCS, vol. 9470, pp. 74–88. Springer, Heidelberg (2015). https://doi.org/10.1007/978-3-662-48995-6_6

    Chapter  Google Scholar 

  6. Auletta, V., Caragiannis, I., Ferraioli, D., Galdi, C., Persiano, G.: Generalized discrete preference games. In: IJCAI, pp. 53–59 (2016)

    Google Scholar 

  7. Auletta, V., Caragiannis, I., Ferraioli, D., Galdi, C., Persiano, G.: Information retention in heterogeneous majority dynamics. In: Devanur, N.R., Lu, P. (eds.) WINE 2017. LNCS, vol. 10660, pp. 30–43. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-71924-5_3

    Chapter  Google Scholar 

  8. Auletta, V., Caragiannis, I., Ferraioli, D., Galdi, C., Persiano, G.: Robustness in discrete preference games. In: AAMAS, pp. 1314–1322 (2017)

    Google Scholar 

  9. Auletta, V., Fanelli, A., Ferraioli, D.: Consensus in opinion formation processes in fully evolving environments. In: AAAI, pp. 6022–6029 (2019)

    Google Scholar 

  10. Auletta, V., Ferraioli, D., Greco, G.: On the complexity of reasoning about opinion diffusion under majority dynamics. Artif. Intell. 284, 103288 (2020)

    Article  MathSciNet  Google Scholar 

  11. Auletta, V., Ferraioli, D., Greco, G.: Optimal majority dynamics for the diffusion of an opinion when multiple alternatives are available. Theoret. Comput. Sci. 869, 156–180 (2021)

    Article  MathSciNet  Google Scholar 

  12. Auletta, V., Ferraioli, D., Savarese, V.: Manipulating an election in social networks through edge addition. In: Alviano, M., Greco, G., Scarcello, F. (eds.) AI*IA 2019. LNCS (LNAI), vol. 11946, pp. 495–510. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-35166-3_35

    Chapter  Google Scholar 

  13. Bakshy, E., Messing, S., Adamic, L.A.: Exposure to ideologically diverse news and opinion on Facebook. Science 348(6239), 1130–1132 (2015)

    Article  MathSciNet  Google Scholar 

  14. Benigni, M.C., Joseph, K., Carley, K.M.: Online extremism and the communities that sustain it: detecting the ISIS supporting community on Twitter. PLoS ONE 12, 1–23 (2017)

    Article  Google Scholar 

  15. Bhawalkar, K., Gollapudi, S., Munagala, K.: Coevolutionary opinion formation games. In: STOC, pp. 41–50 (2013)

    Google Scholar 

  16. Bilò, V., Fanelli, A., Moscardelli, L.: Opinion formation games with dynamic social influences. Theoret. Comput. Sci. 746, 444–458 (2018)

    Article  MathSciNet  Google Scholar 

  17. Bindel, D., Kleinberg, J.M., Oren, S.: How bad is forming your own opinion? Games Econom. Behav. 92, 248–265 (2015)

    Article  MathSciNet  Google Scholar 

  18. Bredereck, R., Elkind, E.: Manipulating opinion diffusion in social networks. In: IJCAI, pp. 894–900 (2017)

    Google Scholar 

  19. Castiglioni, M., Ferraioli, D., Gatti, N., Landriani, G.: Election manipulation on social networks: seeding, edge removal, edge addition. J. Artif. Intell. Res. 71, 1049–1090 (2021)

    Article  MathSciNet  Google Scholar 

  20. Chierichetti, F., Kleinberg, J., Oren, S.: On discrete preferences and coordination. J. Comput. Syst. Sci. 93, 11–29 (2018)

    Article  MathSciNet  Google Scholar 

  21. Corò, F., Cruciani, E., D’Angelo, G., Ponziani, S.: Exploiting social influence to control elections based on scoring rules. In: IJCAI, pp. 201–207 (2019)

    Google Scholar 

  22. Dandekar, P., Goel, A., Lee, D.T.: Biased assimilation, homophily, and the dynamics of polarization. Proc. Natl. Acad. Sci. 110(15), 5791–5796 (2013)

    Article  MathSciNet  Google Scholar 

  23. DeGroot, M.: Reaching a consensus. J. Am. Stat. Assoc. 69(345), 118–121 (1974)

    Article  Google Scholar 

  24. Feldman, M., Immorlica, N., Lucier, B., Weinberg, S.M.: Reaching consensus via non-Bayesian asynchronous learning in social networks. In: APPROX/RANDOM, pp. 192–208 (2014)

    Google Scholar 

  25. Ferraioli, D., Goldberg, P., Ventre, C.: Decentralized dynamics for finite opinion games. Theoret. Comput. Sci. 648, 96–115 (2016)

    Article  MathSciNet  Google Scholar 

  26. Ferraioli, D., Ventre, C.: Social pressure in opinion games. In: IJCAI, pp. 3661–3667 (2017)

    Google Scholar 

  27. Fotakis, D., Kandiros, V., Kontonis, V., Skoulakis, S.: Opinion dynamics with limited information. In: Christodoulou, G., Harks, T. (eds.) WINE 2018. LNCS, vol. 11316, pp. 282–296. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-04612-5_19

    Chapter  Google Scholar 

  28. Fotakis, D., Palyvos-Giannas, D., Skoulakis, S.: Opinion dynamics with local interactions. In: IJCAI, pp. 279–285 (2016)

    Google Scholar 

  29. Friedkin, N., Johnsen, E.: Social influence and opinions. J. Math. Sociol. 15(3–4), 193–206 (1990)

    Article  Google Scholar 

  30. Fujiwara, T., Müller, K., Schwarz, C.: The effect of social media on elections: Evidence from the united states. Tech. rep, National Bureau of Economic Research (2021)

    Google Scholar 

  31. Gilbert, E.N.: Random graphs. Ann. Math. Stat. 30(4), 1141–1144 (1959)

    Article  Google Scholar 

  32. Halberstam, Y., Knight, B.: Homophily, group size, and the diffusion of political information in social networks: evidence from Twitter. J. Public Econ. 143, 73–88 (2016)

    Article  Google Scholar 

  33. Hegselmann, R., Krause, U.: Opinion dynamics and bounded confidence: models, analysis and simulation. J. Artif. Soc. Soc. Simul. 5, 1–24 (2002)

    Google Scholar 

  34. Kempe, D., Kleinberg, J., Tardos, E.: Maximizing the spread of influence through a social network. In: KDD, pp. 137–146 (2003)

    Google Scholar 

  35. Kernighan, B.W., Lin, S.: An efficient heuristic procedure for partitioning graphs. Bell Syst. Tech. J. 49(2), 291–307 (1970)

    Article  Google Scholar 

  36. Krioukov, D., Papadopoulos, F., Kitsak, M., Vahdat, A., Boguná, M.: Hyperbolic geometry of complex networks. Phys. Rev. E 82(3), 036106 (2010)

    Article  MathSciNet  Google Scholar 

  37. Leskovec, J., Krevl, A.: SNAP datasets: Stanford large network dataset collection (2014). http://snap.stanford.edu/data

  38. Levy, R.: Social media, news consumption, and polarization: evidence from a field experiment. Am. Econ. Rev. 111(3), 831–70 (2021)

    Article  Google Scholar 

  39. Lin, Z., Francis, B., Maggiore, M.: Necessary and sufficient graphical conditions for formation control of unicycles. IEEE Trans. Autom. Control 50(1), 121–127 (2005)

    Article  MathSciNet  Google Scholar 

  40. McAuley, J.J., Leskovec, J.: Learning to discover social circles in ego networks. In: NIPS, vol. 2012, pp. 548–556 (2012)

    Google Scholar 

  41. Mossel, E., Neeman, J., Tamuz, O.: Majority dynamics and aggregation of information in social networks. Auton. Agent. Multi-Agent Syst. 28(3), 408–429 (2013). https://doi.org/10.1007/s10458-013-9230-4

    Article  Google Scholar 

  42. Olfati-Saber, R.: Flocking for multi-agent dynamic systems: algorithms and theory. IEEE Trans. Autom. Control 51(3), 401–420 (2006)

    Article  MathSciNet  Google Scholar 

  43. Olfati-Saber, R., Shamma, J.S.: Consensus filters for sensor networks and distributed sensor fusion. In: CDC, pp. 6698–6703 (2005)

    Google Scholar 

  44. Rozemberczki, B., Sarkar, R.: Characteristic functions on graphs: birds of a feather, from statistical descriptors to parametric models. In: CIKM, pp. 1325–1334 (2020)

    Google Scholar 

  45. Savkin, A.V.: Coordinated collective motion of groups of autonomous mobile robots: analysis of Vicsek’s model. IEEE Trans. Autom. Control 49(6), 981–982 (2004)

    Article  MathSciNet  Google Scholar 

  46. Shearer, E., Matsa, K.E.: News use across social media platforms 2018 (2018). https://www.pewresearch.org

  47. Sina, S., Hazon, N., Hassidim, A., Kraus, S.: Adapting the social network to affect elections. In: AAMAS, pp. 705–713 (2015)

    Google Scholar 

  48. Tanner, H.G., Pappas, G.J., Kumar, V.: Leader-to-formation stability. IEEE Trans. Robot. Autom. 20(3), 443–455 (2004)

    Article  Google Scholar 

  49. Watts, D.J., Strogatz, S.H.: Collective dynamics of ‘small-world’ networks. Nature 393(6684), 440–442 (1998)

    Article  Google Scholar 

  50. Wilder, B., Vorobeychik, Y.: Controlling elections through social influence. In: AAMAS, pp. 265–273 (2018)

    Google Scholar 

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Authors and Affiliations

  1. Università degli Studi di Salerno, Salerno, Italy

    Vincenzo Auletta, Antonio Coppola & Diodato Ferraioli

Authors
  1. Vincenzo Auletta
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  2. Antonio Coppola
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  3. Diodato Ferraioli
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Corresponding author

Correspondence to Antonio Coppola .

Editor information

Editors and Affiliations

  1. Department of Informatics, Systems and Communication, University of Milano-Bicocca, Milan, Italy

    Stefania Bandini

  2. Department of Informatics, Systems and Communication, University of Milano-Bicocca, Milan, Italy

    Francesca Gasparini

  3. Department of Informatics, Bioengineering, Robotics and Systems Engineering, University of Genoa, Genova, Italy

    Viviana Mascardi

  4. Department of Informatics, Systems and Communication, University of Milano-Bicocca, Milan, Italy

    Matteo Palmonari

  5. Department of Informatics, Systems and Communication, University of Milano-Bicocca, Milan, Italy

    Giuseppe Vizzari

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Auletta, V., Coppola, A., Ferraioli, D. (2022). On the Impact of Social Media Recommendations on Opinion Consensus. In: Bandini, S., Gasparini, F., Mascardi, V., Palmonari, M., Vizzari, G. (eds) AIxIA 2021 – Advances in Artificial Intelligence. AIxIA 2021. Lecture Notes in Computer Science(), vol 13196. Springer, Cham. https://doi.org/10.1007/978-3-031-08421-8_18

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  • DOI: https://doi.org/10.1007/978-3-031-08421-8_18

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