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Mining Social Networks for Dissemination of Fake News Using Continuous Opinion-Based Hybrid Model

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Advanced Data Mining and Applications (ADMA 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13087))

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Abstract

The entire world is confronting the challenge of fake news disseminated online, as its consequences could be exceptionally catastrophic. In this paper, we have proposed a hybrid model that integrates the opinion evolution process with the propagation of fake news. The level of extremity in opinions, the amount of support from social connections and the social influence were used as the major design considerations in modeling the spread of fake news. As polarized opinions on social media often lead to polarized networks, the proposed model was utilized to study the effect of evolving opinion on the spread of fake news on polarized networks of varying degrees. Our findings suggested that there are more users involved in sharing fake news in the presence of a highly polarized network. Moreover, the tendency of a user to adapt the opposing opinion seems to be correlated with the exposure of fake news. Besides this, we also assessed the consequences of the spread of fake news on the user’s opinion and found that the users that are mainly influenced are the ones having an unclear stance towards a given issue. Overall, our proposed model highlights the interrelation between fake news and the opinion evolution on social networks.

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Notes

  1. 1.

    Code for the model is available at https://github.com/maneetsingh88/fakenewsModeling

References

  1. Arafa, M.: The archeology of freedom of information laws: Egypt and fake-news laws. Fla. Coastal L. Rev. 20, 73 (2020)

    Google Scholar 

  2. Blondel, V.D., Guillaume, J.L., Lambiotte, R., Lefebvre, E.: Fast unfolding of communities in large networks. J. Stat. Mech: Theory Exp. 2008(10), P10008 (2008)

    Article  Google Scholar 

  3. Chen, J., Song, Q., Zhou, Z.: Agent-based simulation of rumor propagation on social network based on active immune mechanism. J. Syst. Sci. Inf. 5(6), 571–584 (2017)

    Google Scholar 

  4. Del Vicario, M., Bessi, A., Zollo, F., Petroni, F., Scala, A., Caldarelli, G., Stanley, H.E., Quattrociocchi, W.: The spreading of misinformation online. Proc. Natl. Acad. Sci. 113(3), 554–559 (2016)

    Article  Google Scholar 

  5. Friggeri, A., Adamic, L., Eckles, D., Cheng, J.: Rumor cascades. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8 (2014)

    Google Scholar 

  6. Garimella, K., Morales, G.D.F., Gionis, A., Mathioudakis, M.: Quantifying controversy on social media. ACM Trans. Soc. Comput. 1(1), 1–27 (2018)

    Article  Google Scholar 

  7. Grinberg, N., Joseph, K., Friedland, L., Swire-Thompson, B., Lazer, D.: Fake news on twitter during the 2016 us presidential election. Science 363(6425), 374–378 (2019)

    Article  Google Scholar 

  8. Han, S., Zhuang, F., He, Q., Shi, Z., Ao, X.: Energy model for rumor propagation on social networks. Physica A 394, 99–109 (2014)

    Article  Google Scholar 

  9. Indu, V., Thampi, S.M.: A nature-inspired approach based on forest fire model for modeling rumor propagation in social networks. J. Netw. Comput. Appl. 125, 28–41 (2019)

    Article  Google Scholar 

  10. Kucharski, A.: Study epidemiology of fake news. Nature 540(7634), 525–525 (2016)

    Article  Google Scholar 

  11. Li, M.Y., Muldowney, J.S.: Global stability for the seir model in epidemiology. Math. Biosci. 125(2), 155–164 (1995)

    Article  MathSciNet  Google Scholar 

  12. Lužar, B., Levnajić, Z., Povh, J., Perc, M.: Community structure and the evolution of interdisciplinarity in slovenia’s scientific collaboration network. PLoS ONE 9(4), e94429 (2014)

    Article  Google Scholar 

  13. Schmidt, A.L., Zollo, F., Scala, A., Betsch, C., Quattrociocchi, W.: Polarization of the vaccination debate on facebook. Vaccine 36(25), 3606–3612 (2018)

    Article  Google Scholar 

  14. Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nat. Commun. 9(1), 1–9 (2018)

    Article  Google Scholar 

  15. Shu, K., Wang, S., Liu, H.: Understanding user profiles on social media for fake news detection. In: 2018 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR), pp. 430–435. IEEE (2018)

    Google Scholar 

  16. Singh, M., Kaur, R., Iyengar, S.R.S.: Multidimensional analysis of fake news spreaders on Twitter. In: Chellappan, S., Choo, K.-K.R., Phan, N.H. (eds.) CSoNet 2020. LNCS, vol. 12575, pp. 354–365. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-66046-8_29

    Chapter  Google Scholar 

  17. Smith-Roberts, A.: Facebook, fake news, and the first amendment. In: Denver Law Review Forum, vol. 95, p. 21 (2018)

    Google Scholar 

  18. Törnberg, P.: Echo chambers and viral misinformation: modeling fake news as complex contagion. PLoS ONE 13(9), e0203958 (2018)

    Google Scholar 

  19. Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018)

    Article  Google Scholar 

  20. Weisbuch, G.: Bounded confidence and social networks. Eur. Phys. J. B 38(2), 339–343 (2004). https://doi.org/10.1140/epjb/e2004-00126-9

    Article  Google Scholar 

  21. Yang, K.C., Pierri, F., Hui, P.M., Axelrod, D., Torres-Lugo, C., Bryden, J., Menczer, F.: The covid-19 infodemic: Twitter versus facebook. Big Data Soc. 8(1), 20539517211013860 (2021)

    Article  Google Scholar 

  22. Zeng, R., Zhu, D.: A model and simulation of the emotional contagion of netizens in the process of rumor refutation. Sci. Rep. 9(1), 1–15 (2019)

    Google Scholar 

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Correspondence to Rishemjit Kaur .

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Singh, M., Iyengar, S.R.S., Kaur, R. (2022). Mining Social Networks for Dissemination of Fake News Using Continuous Opinion-Based Hybrid Model. In: Li, B., et al. Advanced Data Mining and Applications. ADMA 2022. Lecture Notes in Computer Science(), vol 13087. Springer, Cham. https://doi.org/10.1007/978-3-030-95405-5_16

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  • DOI: https://doi.org/10.1007/978-3-030-95405-5_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-95404-8

  • Online ISBN: 978-3-030-95405-5

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