A FRAMEWORK TO FILTER UNWANTED MESSAGES FROM OSNs USER WALL

Volume: 2    Issue: 1

Year of Publication: 2015

Pages: (22-28)

Authors: Kanika Sharma, Prof. Manavjeet Kaur

Download Full Text (PDF)

Abstract

Social networking sites plays a significant role in today’s society, it is now one of the daily activities in everyone’s regular life. With the help of smart phones, its use has increased drastically. At present, online Social Networks does not provide its users the capability to control the messages posted on their own confidential space/private wall, to avoid the unwanted content being displayed. To fill this gap, in the present paper, we suggest a system allowing OSN users to have a direct control on the messages posted onto their wall. This is achieved through a flexible rule-based system, that allows users to specify the filtering criteria to be applied to their walls, and with the help of Machine Learning based soft classifier the short text messages are classified into different categories and can be filtered as desired by the users.

References

1. 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.

2. 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.

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

4. 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.

5. Sriram, D. Fuhry, E. Demir, H. Ferhatosmanoglu, and M. Demirbas, “Short Text Classification in Twitter to Improve Information Filtering,” Proc. 33rd Int’l ACM SIGIR Conf. Research and Development in Information Retrieval (SIGIR ’10), pp. 841-842, 201

6. S. Pollock, “A rule-based message filtering system,” ACM Transactions on Office Information Systems, vol. 6, no. 3, pp. 232–254,1988

7. 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.

8. Sarah Jane Delany,\" SMS spam filtering: Methods and Data \", Sarah Jane Delany, Mark Buckley, Derek Greene (2012) SMS Spam Filtering: Methods and Data, Expert Systems with Applications 39 (10), p9899-9908, Elsevier.

9. Tarek M Mahmoud,\" SMS Spam Filtering Technique Based on Artificial Immune System \", IJCSI International Journal of Computer Science Issues, Vol. 9, Issue 2, No 1, March 2012 ISSN (Online): 1694-0814 www.IJCSI.org.

10. Chen M, Jin X, Shen D., “Short text classification improved by learning multi-granularity topics”. In Proceedings of the Twenty-Second international joint conference on Artificial Intelligence-Volume Volume Three. AAAI Press, 2011:1776-1781.

11. J. Golbeck, “Combining Provenance with Trust in Social Networks for Semantic Web Content Filtering,” Proc. Int’l Conf. Provenance and Annotation of Data, L. Moreau and I. Foster, eds., pp. 101-108, 2006.

12. K. Strater and H. Richter, “Examining Privacy and Disclosure in a Social Networking Community,” Proc. Third Symp. Usable Privacy and Security (SOUPS ’07), pp. 157-158, 2007.

Keywords

Online social networks, Content based filtering and Filtering System.