Abstract
The importance of online social networks as a media for dissemination of news has increased in the last decade. The real time nature of the contents and the speed and volume of propagation have posed great challenges to assess the quality of information in an acceptable time frame. Collusion of users to spread false information and simultaneous spread of multiple false messages have made their detection a challenging task. In this paper we propose a methodology based on principles of cognitive psychology for detecting and monitoring sources who collude with each other to spread misinformation. We use social network as a social computing platform to classify sources as credible or non-credible based on the level of acceptance of their messages by other users and patterns of propagation. The proposed methodology could form a framework for an effective social media monitoring system. We have implemented our algorithm in the online social network ‘Twitter’
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References
Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Transactions onKnowledge and Data Engineering 17(6), 734–749 (2005)
Almazro, D., Shahatah, G., Albdulkarim, L., Kherees, M., Martinez, R., Nzoukou, W.: A survey paper on recommender systems. arXiv preprint arXiv:1006.5278 (2010)
Blondel, V.D., Guillaume, J.L., Lambiotte, R., Lefebvre, E.: Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment 2008(10), P10,008 (2008)
Castillo, C., Mendoza, M., Poblete, B.: Information credibility on twitter. In: Proceedings of the 20th International Conference on World Wide Web, pp. 675–684. ACM (2011)
Colbaugh, R., Glass, K.: Early warning analysis for social diffusion events. Security Informatics 1(1), 1–26 (2012)
Gupta, A., Kumaraguru, P.: Credibility ranking of tweets during high impact events. In: Proceedings of the 1st Workshop on Privacy and Security in Online Social Media, p. 2. ACM (2012)
Hawksey, M.: Twitter Archiving Google Spreadsheet TAGS v5. JISC CETIS MASHe: The Musing of Martin Hawksey, EdTech Explorer (2013), http://mashe.hawksey.info/2013/02/twitter-archive-tagsv5/ (accessed September 2013)
Karlova, N.A., Fisher, K.E.: “Plz RT”: A social diffusion model of misinformation and disinformation for understanding human information behaviour. Information Research 18(1), 1–17 (2013)
Kwak, H., Lee, C., Park, H., Moon, S.: What is twitter, a social network or a news media? In: Proceedings of the 19th International Conference on World Wide Web, pp. 591–600. ACM (2010)
Lewandowsky, S., Ecker, U.K., Seifert, C.M., Schwarz, N., Cook, J.: Misinformation and its correction continued influence and successful debiasing. Psychological Science in the Public Interest 13(3), 106–131 (2012)
Mendoza, M., Poblete, B., Castillo, C.: Twitter under crisis: Can we trust what we rt? In: Proceedings of the First Workshop on Social Media Analytics, pp. 71–79. ACM (2010)
Mobasher, B., Burke, R., Bhaumik, R., Sandvig, J.: Attacks and remedies in collaborative recommendation. Intelligent Systems 22(3), 56–63 (2007)
Mobasher, B., Burke, R., Bhaumik, R., Williams, C.: Effective attack models for shilling item-based collaborative filtering systems. In: Proceedings of the 2005 WebKDD Workshop (2005)
Page, L., Brin, S., Motwani, R., Winograd, T.: The pagerank citation ranking: bringing order to the web (1999)
Su, X., Khoshgoftaar, T.M.: A survey of collaborative filtering techniques. Advances in Artificial Intelligence 2009, 4 (2009)
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Krishna Kumar, K.P., Geethakumari, G. (2014). Identifying Sources of Misinformation in Online Social Networks. In: Thampi, S., Gelbukh, A., Mukhopadhyay, J. (eds) Advances in Signal Processing and Intelligent Recognition Systems. Advances in Intelligent Systems and Computing, vol 264. Springer, Cham. https://doi.org/10.1007/978-3-319-04960-1_37
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DOI: https://doi.org/10.1007/978-3-319-04960-1_37
Publisher Name: Springer, Cham
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