The week's pick
Reseach Article
A Survey on OSN Message Filtering
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 113 - Number 17 |
| Year of Publication: 2015 |
| Authors: Kalpesh Gandhi, Rahul Panditrao, Vibha B. Lahane |
10.5120/19918-2065
|
Kalpesh Gandhi, Rahul Panditrao, Vibha B. Lahane . A Survey on OSN Message Filtering. International Journal of Computer Applications. 113, 17 ( March 2015), 19-22. DOI=10.5120/19918-2065
Abstract
In the specified documents data mining technique has been deprecated for filtering the OSN wall with unwanted messages or any type of vulgar messages. OSN is Online Social Network which has become an important part of the people life these days. People communicate over it with friends, relatives over a OSN wall. Thus to provide a feel of security to users personal stuff it is important to filter the OSN wall for any unwanted message . But the questions Arises, how to filter the OSN wall with a facility provided of blocking unwanted messages on the user's private wall. This can be gained through the flexible rule-based system which implements filtering criteria that can be customized by the user and a Machine Learning-based soft classifier which automatically labels messages in the support of content-based filtering . This paper consist of a literature survey paper of the existing system with proposed system as a technique to filter similar meaning words using Ontology along with the basic functionality to filter the OSN wall for unwanted message. In this paper a technique to build a social network with filtered message is elaborated.
References
- Adomavicius, G. and Tuzhilin, "Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions," IEEE Transaction on Knowledge and Data Engineering, vol. 17, no. 6, pp. 734–749, 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," in Proceedings of the Fifth ACM Conference on Digital Libraries. New York: ACM Press, 2000, pp. 195–204
- F. Sebastiani, "Machine learning in automated text categorization,"ACM Computing Surveys, vol. 34, no. 1, pp. 1–47, 2002. P. J. Denning, "Electronic junk," Communications of the ACM, vol. 25, no. 3, pp. 163–165, 1982.
- N. J. Belkin and W. B. Croft, "Information filtering and information retrieval: Two sides of the same coin?" Communications of the ACM,vol. 35, no. 12, pp. 29–38, 1992.
- P. J. Denning, "Electronic junk," Communications of the 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," Communications of the ACM, vol. 35, no. 12, pp. 51–60, 1992.
- P. S. Jacobs and L. F. Rau, "Scisor: Extracting information from online news," Communications of the ACM, vol. 33, no. 11, pp. 88–97,1990.
- S. Pollock, "A rule-based message filtering system," ACM Transactions on Office Information Systems, vol. 6, no. 3, pp. 232–254,1988.
- P. E. Baclace, "Competitive agents for information filtering," Communications of the 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," in Proceedings of6th IEEE Conference on Artificial Intelligence Applications (CAIA-90). IEEE Computer Society Press, Los Alamitos, US, 1990, pp. 320–326.
- R. E. Schapire and Y. Singer, "Boostexter: a boosting-based system for text categorization," Machine Learning, vol. 39, no. 2/3, pp. 135–168, 2000.
- H. Sch¨utze, D. A. Hull, and J. O. Pedersen, "A comparison of classifiers and document representations for the routing problem," in Proceedings of the 18th Annual ACM/SIGIR Conference on Resea. Springer Verlag, 1995, pp. 229–237.
- T. Joachims, "Text categorization with support vector machines: Learning with many relevant features," in Proceedings of the European Conference on Machine Learning. Springer, 1998, pp. 137–142.
- "A probabilistic analysis of the rocchio algorithm with tfidf for text categorization," in Proceedings of International Conference on Machine Learning, 1997, pp. 143–151.
- S. E. Robertson and K. S. Jones, "Relevance weighting of search terms," Journal of the American Society for Information Science, vol. 27, no. 3, pp. 129–146, 1976.
- S. Zelikovitz and H. Hirsh, "Improving short text classification using unlabeled background knowledge," in Proceedings of 17th International Conference on Machine Learning (ICML-00), P. Langley, Ed. Stanford, US: Morgan Kaufmann Publishers, San Francisco, US,2000, pp. 1183–1190.
- B. Sriram, D. Fuhry, E. Demir, H. Ferhatosmanoglu, and M. Demirbas,"Short text classification in twitter to improve information filtering," in Proceeding of the 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2010, 2010, pp. 841–842.
- J. Golbeck, "Combining provenance with trust in social networks for semantic web content filtering," in Provenance and Annotation of Data, ser. Lecture Notes in Computer Science, L. Moreau and I. Foster, Eds. Springer Berlin / Heidelberg, 2006, vol. 4145, pp. 101–108.
- Marco Vanetti, Elisabetta Binaghi, Elena Ferrari, Barbara Carminati, an Moreno Carullo, "A System to Filter Unwanted Messages from OSN User Walls",2013.
- P. Bonatti and D. Olmedilla, "Driving and monitoring provisional trust negotiation with metapolicies," in In 6th IEEE International Workshop on Policies for Distributed Systems and Networks (POLICY 2005). IEEE Computer Society, 2005, pp. 14–23.
- C. Bizer and R. Cyganiak, "Quality-driven information filtering using the wiqa policy framework," Web Semantics: Science, Services and Agents on the World Wide Web, vol. 7, pp. 1–10, January 2009.
Index Terms
Keywords