A Classification Mechanism To Avoid Useless Data From Osn Walls

S koteswara rao Yarlagadda, CH Hemanand, K.T.V Subbarao


The attempt of the present work is consequently to propose and experimentally estimate an automated system called Filtered Wall (FW) which is competent to filter unwanted messages from OSN user walls. One essential issue in today’s Online Social Networks (OSNs) is to give users the provision to control the messages posted on their own private space to shun that unwanted content is displayed. This is achieved through a flexible rule-based system that let users to adapt the filtering criterion to be applied to their walls and a Machine Learning-based soft classifier automatically labelling messages in support of content-based filtering. The unique set of description imitative from endogenous properties of short texts is distended here including exogenous knowledge connected to the context from which the messages create. As far as the learning model is apprehensive we confirm in the current paper the use of neural learning which is today documented as one of the well-organized solutions in text classification. In particular we base the overall short text classification strategy on Radial Basis Function Networks (RBFN) for their established potential in acting as soft classifiers in managing noisy data and essentially vague classes.



Online social networks, information filtering, short text classification, policy-based personalization.


A. Adomavicius and G. Tuzhilin, “Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions,” IEEE Trans. Knowledge and Data Eng., vol. 17, no. 6, pp. 734-749, June 2005.

M. Chau and H. Chen, “A Machine Learning Approach to Web Page Filtering Using Content and Structure Analysis,” Decision Support Systems, vol. 44, no. 2, pp. 482-494, 2008.

R.J. Mooney and L. Roy, “Content-Based Book Recommending Using Learning for Text Categorization,” Proc. Fifth ACM Conf. Digital Libraries, pp. 195-204, 2000.

F. Sebastiani, “Machine Learning in Automated Text Categorization,” ACM Computing Surveys, vol. 34, no. 1, pp. 1-47, 2002.

M. Vanetti, E. Binaghi, B. Carminati, M. Carullo, and E. Ferrari, “Content-Based Filtering in On-Line Social Networks,” Proc. ECML/PKDD Workshop Privacy and Security Issues in Data Mining

and Machine Learning (PSDML ’10), 2010.

N.J. Belkin and W.B. Croft, “Information Filtering and Information Retrieval: Two Sides of the Same Coin?” Comm. ACM, vol. 35, no. 12, pp. 29-38, 1992.

P.J. Denning, “Electronic Junk,” Comm. ACM, vol. 25, no. 3, pp. 163-165, 1982.

P.W. Foltz and S.T. Dumais, “Personalized Information Delivery: An Analysis of Information Filtering Methods,” Comm. ACM, vol. 35, no. 12, pp. 51-60, 1992.

P.S. Jacobs and L.F. Rau, “Scisor: Extracting Information from On- Line News,” Comm. ACM, vol. 33, no. 11, pp. 88-97, 1990.

S. Pollock, “A Rule-Based Message Filtering System,” ACM Trans. Office Information Systems, vol. 6, no. 3, pp. 232-254, 1988.

P.E. Baclace, “Competitive Agents for Information Filtering,” Comm. ACM, vol. 35, no. 12, p. 50, 1992.

P.J. Hayes, P.M. Andersen, I.B. Nirenburg, and L.M. Schmandt, “Tcs: A Shell for Content-Based Text Categorization,” Proc. Sixth IEEE Conf. Artificial Intelligence Applications (CAIA ’90), pp. 320- 326, 1990.

G. Amati and F. Crestani, “Probabilistic Learning for Selective Dissemination of Information,” Information Processing and Management, vol. 35, no. 5, pp. 633-654, 1999.

M.J. Pazzani and D. Billsus, “Learning and Revising User Profiles: The Identification of Interesting Web Sites,” Machine Learning, vol. 27, no. 3, pp. 313-331, 1997.

Y. Zhang and J. Callan, “Maximum Likelihood Estimation for Filtering Thresholds,” Proc. 24th Ann. Int’l ACM SIGIR Conf. Research and Development in Information Retrieval, pp. 294-302, 2001.

C. Apte, F. Damerau, S.M. Weiss, D. Sholom, and M. Weiss, “Automated Learning of Decision Rules for Text Categorization,” Trans. Information Systems, vol. 12, no. 3, pp. 233-251, 1994.

S. Dumais, J. Platt, D. Heckerman, and M. Sahami, “Inductive Learning Algorithms and Representations for Text Categorization,” Proc. Seventh Int’l Conf. Information and Knowledge Management (CIKM ’98), pp. 148-155, 1998.

D.D. Lewis, “An Evaluation of Phrasal and Clustered Representations on a Text Categorization Task,” Proc. 15th ACM Int’l Conf. Research and Development in Information Retrieval (SIGIR ’92),

N.J. Belkin, P. Ingwersen, and A.M. Pejtersen, eds., pp. 37-50, 1992. [19] R.E. Schapire and Y. Singer, “Boostexter: A Boosting-Based System for Text Categorization,” Machine Learning, vol. 39,

nos. 2/3, pp. 135-168, 2000.

H. Schu¨ tze, D.A. Hull, and J.O. Pedersen, “A Comparison of Classifiers and Document Representations for the Routing Problem,” Proc. 18th Ann. ACM/SIGIR Conf. Research and Development in Information Retrieval , pp. 229-237, 1995.



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