Abstract
User profiling, defined as the inference of user interests, intentions, characteristics, behaviors and preferences, is nowadays one of the most important keys in personalized services on Internet, such as segmented target advertisements. In this paper, we propose a scalable and automated technique for user ontology profiling in social networks by extracting URL content shared by users in tweets. The new approach models a user profile as a semantic ontology where user interests and intentions are represented. OpenDNS and DBpedia collective knowledge databases are utilized in order to find the interests and intentions categories of the user profile ontology, enhancing the performance of our method and taking the collective categorization of the websites. User profile ontology evolves constantly and is populated with assertions of individuals and relationships of interest and intention from these collective knowledge repositories. Experimental results indicate strongly that the proposed method automatically generates, correctly, the interests and intentions of a user profile.
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References
Malhotra, A., Totti, L., Meira Jr., W., Kumaraguru, P., Almeida, V.: Studying User Footprints in Different Online Social Networks. In: 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 1065–1070 (2012)
Tao, X., Li, Y., Lau, R.Y.K., Geva, S.: Ontology-based Specific and Exhaustive User Profiles for Constraint Information Fusion for Multi-agents. In: 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, pp. 264–271 (2010)
Aïmeur, E., Brassard, G., Molins, P.: Reconstructing Profiles from Information Disseminated on the Internet. In: 2012 ASE/IEEE International Conference on Social Computing and International Conference on Privacy, Security, Risk and Trust, pp. 875–883 (2012)
Pennacchiotti, M., Popescu, A.: Democrats, Republicans and Starbucks Afficionados: User Classification in Twitter. In: Proceedings of the 17th ACM SIGKDD, pp. 430–438 (2011)
Pennacchiotti, M., Popescu, A.: A Machine Learning Approach to Twitter User Classification. In: Proceedings of the Fifth International AAAI Conference on Weblogs and SocialMedia, pp. 281–288 (2011)
Dey, L., Gaonkar, B.: Discovering regular and consistent behavioral patterns in topical tweeting. In: 21st International Conference on Pattern Recognition (ICPR 2012), pp. 3464–3467 (2012)
Wagner, C., Liao, V., Pirolli, P., Nelson, L., Strohmaier, M.: It’s not in their tweets: Modeling topical expertise of Twitter users. In: 2012 ASE/IEEE International Conference on Social Computing and 2012 ASE/IEEE International Conference on Privacy, Security, Risk and Trust, pp. 91–100 (2012)
Quercia, D., Kosinski, M., Stillwell, D., Crowcroft, J.: Our Twitter Profiles, Our Selves: Predicting Personality with Twitter. In: 2011 IEEE International Conference on Privacy, Security, Risk, and Trust and IEEE International Conference on Social Computing, pp. 180–185 (2011)
Siehndel, P., Kawase, R.: TwikiMe! - User profiles that make sense. In: 11th International Semantic Web Conference (ISWC 2012) (2012)
Tao, K., Abel, F., Gao, Q., Houben, G.-J.: TUMS: Twitter-Based User Modeling Service. In: García-Castro, R., Fensel, D., Antoniou, G. (eds.) ESWC 2011. LNCS, vol. 7117, pp. 269–283. Springer, Heidelberg (2012)
Sakaki, T., Okazaki, M., Matsuo, Y.: Earthquake Shakes Twitter Users: Real-time Event Detection by Social Sensors. In: 19th International World Wide Web Conference Committee (IW3C2), pp. 851–860 (2010)
Lee, B., Hwang, B.-Y.A.: Study of the Correlation between the Spatial Attributes on Twitter. In: 28th International Conference on Data Engineering Workshops, pp. 337–340 (2012)
Li, R., Wang, S., Deng, H., Wang, R., Chen-Chuan, C.K.: Towards Social User Profiling:Unified and Discriminative Influence Model for Inferring Home Locations. In: ACM International Conference on Knowledge Discovery and Data Mining, pp. 1023–1031 (2012)
Lauschke, C., Ntoutsi, E.: Monitoring User Evolution in Twitter. In: 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 972–977 (2012)
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Peña, P., Del Hoyo, R., Vea-Murguía, J., González, C., Mayo, S. (2013). Automatic Ontology User Profiling for Social Networks from URLs Shared. In: Bielza, C., et al. Advances in Artificial Intelligence. CAEPIA 2013. Lecture Notes in Computer Science(), vol 8109. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40643-0_18
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DOI: https://doi.org/10.1007/978-3-642-40643-0_18
Publisher Name: Springer, Berlin, Heidelberg
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