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Content-Based Filtering in On-Line Social Networks

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Part of the Lecture Notes in Computer Science book series (LNAI,volume 6549)

Abstract

This paper proposes a system enforcing content-based message filtering for On-line Social Networks (OSNs). The system allows OSN users to have a direct control on the messages posted on their walls. This is achieved through a flexible rule-based system, that allows a user to customize the filtering criteria to be applied to their walls, and a Machine Learning based soft classifier automatically labelling messages in support of content-based filtering.

Keywords

  • On-line Social Networks
  • Short Text Classification
  • Text Filtering
  • Filtering Policies

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  • DOI: 10.1007/978-3-642-19896-0_11
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References

  1. Ali, B., Villegas, W., Maheswaran, M.: A trust based approach for protecting user data in social networks. In: Proceedings of the 2007 Conference of the Center for Advanced Studies on Collaborative Research, pp. 288–293. ACM, New York (2007)

    CrossRef  Google Scholar 

  2. Amati, G., Crestani, F.: Probabilistic learning for selective dissemination of information. Information Processing and Management 35(5), 633–654 (1999)

    CrossRef  Google Scholar 

  3. Boykin, P.O., Roychowdhury, V.P.: Leveraging social networks to fight spam. IEEE Computer Magazine 38, 61–67 (2005)

    CrossRef  Google Scholar 

  4. Carminati, B., Ferrari, E.: Access control and privacy in web-based social networks. International Journal of Web Information Systems 4, 395–415 (2008)

    CrossRef  Google Scholar 

  5. Carminati, B., Ferrari, E., Perego, A.: Enforcing access control in web-based social networks. ACM Trans. Inf. Syst. Secur. 13(1), 1–38 (2009)

    CrossRef  MATH  Google Scholar 

  6. Carullo, M., Binaghi, E., Gallo, I.: An online document clustering technique for short web contents. Pattern Recognition Letters 30, 870–876 (2009)

    CrossRef  Google Scholar 

  7. Churcharoenkrung, N., Kim, Y.S., Kang, B.H.: Dynamic web content filtering based on user’s knowledge. In: International Conference on Information Technology: Coding and Computing, vol. 1, pp. 184–188 (2005)

    Google Scholar 

  8. Fang, L., LeFevre, K.: Privacy wizards for social networking sites. In: WWW 2010: Proceedings of the 19th International Conference on World Wide Web, pp. 351–360. ACM, New York (2010)

    Google Scholar 

  9. Fong, P.W.L., Anwar, M.M., Zhao, Z.: A privacy preservation model for facebook-style social network systems. In: Backes, M., Ning, P. (eds.) ESORICS 2009. LNCS, vol. 5789, pp. 303–320. Springer, Heidelberg (2009)

    CrossRef  Google Scholar 

  10. Frakes, W., Baeza-Yates, R. (eds.): Information Retrieval: Data Structures & Algorithms. Prentice-Hall, Englewood Cliffs (1992)

    Google Scholar 

  11. Golbeck, J.A.: Computing and Applying Trust in Web-based Social Networks. Ph.D. thesis, Graduate School of the University of Maryland, College Park (2005)

    Google Scholar 

  12. Hanani, U., Shapira, B., Shoval, P.: Information filtering: Overview of issues, research and systems. User Modeling and User-Adapted Interaction 11, 203–259 (2001)

    CrossRef  MATH  Google Scholar 

  13. Kim, Y.H., Hahn, S.Y., Zhang, B.T.: Text filtering by boosting naive bayes classifiers. In: SIGIR 2000: Proceedings of the 23rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 168–175. ACM, New York (2000)

    Google Scholar 

  14. Landis, J.R., Koch, G.: The measurement of observer agreement for categorical data. Biometrics 33(1), 159–174 (1977)

    CrossRef  MATH  Google Scholar 

  15. Laudanna, A., Thornton, A., Brown, G., Burani, C., Marconi, L.: Un corpus dell’italiano scritto contemporaneo dalla parte del ricevente. III Giornate Internazionali di Analisi Statistica dei Dati Testuali 1, 103–109 (1995)

    Google Scholar 

  16. Lewis, D.D., Yang, Y., Rose, T.G., Li, F.: RCV1: A new benchmark collection for text categorization research. Journal of Machine Learning Research (2004)

    Google Scholar 

  17. Manning, C., Raghavan, P., Schütze, H.: Introduction to Information Retrieval. Cambridge University Press, Cambridge (2008)

    CrossRef  MATH  Google Scholar 

  18. Moody, J., Darken, C.: Fast learning in networks of locally-tuned processing units. Neural Computation 1, 281–294 (1989)

    CrossRef  Google Scholar 

  19. Pérez-Alcázar, J.d.J., Calderón-Benavides, M.L., González-Caro, C.N.: Towards an information filtering system in the web integrating collaborative and content based techniques. In: LA-WEB 2003: Proceedings of the First Conference on Latin American Web Congress, p. 222. IEEE Computer Society, Washington (2003)

    Google Scholar 

  20. Salton, G., Buckley, C.: Term-weighting approaches in automatic text retrieval. Information Processing and Management 24(5), 513–523 (1988)

    CrossRef  Google Scholar 

  21. Sebastiani, F.: Machine learning in automated text categorization. ACM Computing Surveys 34(1), 1–47 (2002)

    CrossRef  Google Scholar 

  22. Strater, K., Richter, H.: Examining privacy and disclosure in a social networking community. In: SOUPS 2007: Proceedings of the 3rd Symposium on Usable Privacy and Security, pp. 157–158. ACM, New York (2007)

    Google Scholar 

  23. Tootoonchian, A., Gollu, K.K., Saroiu, S., Ganjali, Y., Wolman, A.: Lockr: social access control for web 2.0. In: WOSP 2008: Proceedings of the First Workshop on Online Social Networks, pp. 43–48. ACM, New York (2008)

    CrossRef  Google Scholar 

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Vanetti, M., Binaghi, E., Carminati, B., Carullo, M., Ferrari, E. (2011). Content-Based Filtering in On-Line Social Networks. In: Dimitrakakis, C., Gkoulalas-Divanis, A., Mitrokotsa, A., Verykios, V.S., Saygin, Y. (eds) Privacy and Security Issues in Data Mining and Machine Learning. PSDML 2010. Lecture Notes in Computer Science(), vol 6549. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19896-0_11

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  • DOI: https://doi.org/10.1007/978-3-642-19896-0_11

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-19895-3

  • Online ISBN: 978-3-642-19896-0

  • eBook Packages: Computer ScienceComputer Science (R0)