Abstract
The emergence of online social networks as an important media for communication and information dissemination during the last decade has also seen the increase in abuse of the media to spread misinformation, disinformation and propaganda. Detecting the types of semantic attacks possible in online social networks would require their accurate classification. Drawing similarities with other social computing systems like Recommender systems, this paper proposes a new taxonomy for semantic attacks in social networks. Further, we propose an algorithm which uses social network as a medium for social computing to analyse the patterns of propagation of information and identify sources of misinformation in them. We construct a new information propagation graph from the social network data and carry out k-core decomposition of the graph to isolate possible contents of misinformation and the user nodes which are involved in their propagation. We used seven different data sets obtained from ‘Twitter’ to validate our results.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
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 on Knowledge and Data Engineering 17(6), 734–749 (2005)
Alvarez-Hamelin, J.I., Dall’Asta, L., Barrat, A., Vespignani, A.: k-core decomposition: A tool for the visualization of large scale networks. arXiv preprint cs/0504107 (2005)
Bakshy, E., Hofman, J.M., Mason, W.A., Watts, D.J.: Everyone’s an influencer: quantifying influence on twitter. In: Proceedings of the 4th International Conference on Web Search and Data Mining, pp. 65–74. ACM (2011)
Bastian, M., Heymann, S., Jacomy, M.: Gephi: an open source software for exploring and manipulating networks. In: International AAAI Conference on Weblogs and Social Media, pp. 361–362 (2011)
Burke, R., Mobasher, B., Williams, C., Bhaumik, R.: Classification features for attack detection in collaborative recommender systems. In: Proceedings of the 12th International Conference on Knowledge Discovery and Data Mining, pp. 542–547. ACM SIGKDD (2006)
Fallis, D.: A conceptual analysis of disinformation. In: iConference. Chapel Hill, NC (2009)
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/ (last accessed December 29, 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)
OMahony, M.P., Hurley, N.J., Silvestre, G.C.: Attacking recommender systems: The cost of promotion. In: Proceedings of the Workshop on Recommender Systems, in Conjunction with the 17th European Conference on Artificial Intelligence, pp. 24–28. Citeseer (2006)
Ratkiewicz, J., Conover, M., Meiss, M., Gonçalves, B., Patil, S., Flammini, A., Menczer, F.: Truthy: mapping the spread of astroturf in microblog streams. In: Proceedings of the 20th International Conference Companion on World Wwide Web, pp. 249–252. ACM (2011)
Sakaki, T., Okazaki, M., Matsuo, Y.: Earthquake shakes twitter users: real-time event detection by social sensors. In: Proceedings of the 19th International Conference on World Wide Web, pp. 851–860. ACM (2010)
Solis, B.: The information divide: The socialisation of news (2010), http://www.briansolis.com/2010/02/the-information-divide-the-socialization-of-news-and-dissemination/ (last accessed December 29, 2013)
Stahl, B.C.: On the difference or equality of information, misinformation, and disinformation: A critical research perspective. Informing Science: International Journal of an Emerging Transdiscipline 9, 83–96 (2006)
Starbird, K., Palen, L.: (How) will the revolution be retweeted?: Information diffusion and the 2011 Egyptian uprising. In: Proceedings of the International Conference on Computer Supported Cooperative Work, pp. 7–16. ACM (2012)
Zhang, F.: Reverse bandwagon profile inject attack against recommender systems. In: Proceedings of the 2nd International Symposium on Computational Intelligence and Design (ISCID), pp. 15–18. IEEE (2009)
Zhang, F.: Analysis of bandwagon and average hybrid attack model against trust-based recommender systems. In: Proceedinga of the 5th International Conference on Management of e-Commerce and e-Government, pp. 269–273. IEEE (2011)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kumar, K.P.K., Geethakumari, G. (2014). Analysis of Semantic Attacks in Online Social Networks. In: Martínez Pérez, G., Thampi, S.M., Ko, R., Shu, L. (eds) Recent Trends in Computer Networks and Distributed Systems Security. SNDS 2014. Communications in Computer and Information Science, vol 420. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-54525-2_4
Download citation
DOI: https://doi.org/10.1007/978-3-642-54525-2_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-54524-5
Online ISBN: 978-3-642-54525-2
eBook Packages: Computer ScienceComputer Science (R0)