Abstract
Due to the extensive role of social networks in social media, it is easy for people to share the news, and it spreads faster than ever before. These platforms also have been exploited to share the rumor or fake information, which is a threat to society. One method to reduce the impact of fake information is making people aware of the correct information based on hard proof. In this work, first, we propose a propagation model called Competitive Independent Cascade Model with users’ Bias (CICMB) that considers the presence of strong user bias towards different opinions, believes, or political parties. We further propose a method, called \(k-TruthScore\), to identify an optimal set of truth campaigners from a given set of prospective truth campaigners to minimize the influence of rumor spreaders on the network. We compare \(k-TruthScore\) with state of the art methods, and we measure their performances as the percentage of the saved nodes (nodes that would have believed in the fake news in the absence of the truth campaigners). We present these results on a few real-world networks, and the results show that \(k-TruthScore\) method outperforms baseline methods.
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Google trends fake news. Accessed 11 September 2020
Amoruso, M., Anello, D., Auletta, V., Ferraioli, D.: Contrasting the spread of misinformation in online social networks. In: Proceedings of the 16th Conference on Autonomous Agents and MultiAgent Systems, pp. 1323–1331. International Foundation for Autonomous Agents and Multiagent Systems (2017)
Andrews, C., Fichet, E., Ding, Y., Spiro, E.S., Starbird, K.: Keeping up with the tweet-dashians: the impact of’official’accounts on online rumoring. In: Proceedings of the 19th ACM Conference on Computer-Supported Cooperative Work and Social Computing, pp. 452–465. ACM (2016)
Budak, C., Agrawal, D., El Abbadi, A.: Limiting the spread of misinformation in social networks. In: Proceedings of the 20th International Conference on World Wide Web, pp. 665–674. ACM (2011)
Chen, X., Sin, S.C.J., Theng, Y.L., Lee, C.S.: Why do social media users share misinformation? In: Proceedings of the 15th ACM/IEEE-CS Joint Conference on Digital Libraries, pp. 111–114. ACM (2015)
De Choudhury, M., Lin, Y.R., Sundaram, H., Candan, K.S., Xie, L., Kelliher, A.: How does the data sampling strategy impact the discovery of information diffusion in social media? In: Fourth International AAAI Conference on Weblogs and Social Media (2010)
De Choudhury, M., Sundaram, H., John, A., Seligmann, D.D.: Social synchrony: predicting mimicry of user actions in online social media. In: 2009 International Conference on Computational Science and Engineering, vol. 4, pp. 151–158. IEEE (2009)
Farajtabar, M., et al.: Fake news mitigation via point process based intervention. https://arxiv.org/abs/1703.07823 (2017)
Forum, W.E.: The global risks report 2017
Halimeh, A.A., Pourghomi, P., Safieddine, F.: The impact of Facebook’s news fact-checking on information quality (IQ) shared on social media (2017)
Kempe, D., Kleinberg, J., Tardos, É.: Influential nodes in a diffusion model for social networks. In: Caires, L., Italiano, G.F., Monteiro, L., Palamidessi, C., Yung, M. (eds.) ICALP 2005. LNCS, vol. 3580, pp. 1127–1138. Springer, Heidelberg (2005). https://doi.org/10.1007/11523468_91
Kim, J.H., Bock, G.W.: A study on the factors affecting the behavior of spreading online rumors: focusing on the rumor recipient’s emotions. In: PACIS, p. 98 (2011)
Lee, C., Shin, J., Hong, A.: Does social media use really make people politically polarized? Direct and indirect effects of social media use on political polarization in South Korea. Telemat. Inform. 35(1), 245–254 (2018)
Mosseri, A.: Working to stop misinformation and false news (2017). https://newsroom.fb.com/news/2017/04/working-to-stop-misinformation-and-false-news/
Nguyen, N.P., Yan, G., Thai, M.T., Eidenbenz, S.: Containment of misinformation spread in online social networks. In: Proceedings of the 4th Annual ACM Web Science Conference, pp. 213–222. ACM (2012)
Ozturk, P., Li, H., Sakamoto, Y.: Combating rumor spread on social media: the effectiveness of refutation and warning. In: 2015 48th Hawaii International Conference on System Sciences (HICSS), pp. 2406–2414. IEEE (2015)
Park, J., Cha, M., Kim, H., Jeong, J.: Managing bad news in social media: a case study on domino’s pizza crisis. In: ICWSM, vol. 12, pp. 282–289 (2012)
Pham, C.V., Phu, Q.V., Hoang, H.X.: Targeted misinformation blocking on online social networks. In: Nguyen, N.T., Hoang, D.H., Hong, T.-P., Pham, H., Trawiński, B. (eds.) ACIIDS 2018. LNCS (LNAI), vol. 10751, pp. 107–116. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-75417-8_10
Pham, C.V., Phu, Q.V., Hoang, H.X., Pei, J., Thai, M.T.: Minimum budget for misinformation blocking in online social networks. J. Comb. Optim. 38(4), 1101–1127 (2019). https://doi.org/10.1007/s10878-019-00439-5
Saxena, A., Hsu, W., Lee, M.L., Leong Chieu, H., Ng, L., Teow, L.N.: Mitigating misinformation in online social network with top-k debunkers and evolving user opinions. In: Companion Proceedings of the Web Conference, pp. 363–370 (2020)
Soares, F.B., Recuero, R., Zago, G.: Influencers in polarized political networks on twitter. In: Proceedings of the 9th International Conference on Social Media and Society, pp. 168–177 (2018)
Song, C., Hsu, W., Lee, M.: Temporal influence blocking: minimizing the effect of misinformation in social networks. In: 33rd IEEE International Conference on Data Engineering, ICDE 2017, San Diego, CA, USA, 19–22 April 2017, pp. 847–858 (2017). https://doi.org/10.1109/ICDE.2017.134
Tanaka, Y., Sakamoto, Y., Matsuka, T.: Toward a social-technological system that inactivates false rumors through the critical thinking of crowds. In: 2013 46th Hawaii International Conference on System Sciences (HICSS), pp. 649–658. IEEE (2013)
Viswanath, B., Mislove, A., Cha, M., Gummadi, K.P.: On the evolution of user interaction in Facebook. In: Proceedings of the 2nd ACM Workshop on Online Social Networks, pp. 37–42. ACM (2009)
Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018)
Wang, Yu., Feng, Y., Hong, Z., Berger, R., Luo, J.: How polarized have we become? A multimodal classification of Trump followers and Clinton followers. In: Ciampaglia, G.L., Mashhadi, A., Yasseri, T. (eds.) SocInfo 2017. LNCS, vol. 10539, pp. 440–456. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-67217-5_27
Yang, L., Li, Z., Giua, A.: Influence minimization in linear threshold networks. Automatica 100, 10–16 (2019)
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Saxena, A., Saxena, H., Gera, R. (2020). k-TruthScore: Fake News Mitigation in the Presence of Strong User Bias. In: Chellappan, S., Choo, KK.R., Phan, N. (eds) Computational Data and Social Networks. CSoNet 2020. Lecture Notes in Computer Science(), vol 12575. Springer, Cham. https://doi.org/10.1007/978-3-030-66046-8_10
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DOI: https://doi.org/10.1007/978-3-030-66046-8_10
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