JOURNAL BROWSE
Search
Advanced SearchSearch Tips
Improved Feature Extraction Method for the Contents Polluter Detection in Social Networking Service
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
Improved Feature Extraction Method for the Contents Polluter Detection in Social Networking Service
Han, Jin Seop; Park, Byung Joon;
  PDF(new window)
 Abstract
The number of users of SNS such as Twitter and Facebook increases due to the development of internet and the spread of supply of mobile devices such as smart phone. Moreover, there are also an increasing number of content pollution problems that pollute SNS by posting a product advertisement, defamatory comment and adult contents, and so on. This paper proposes an improved method of extracting the feature of content polluter for detecting a content polluter in SNS. In particular, this paper presents a method of extracting the feature of content polluter on the basis of incremental approach that considers only increment in data, not batch processing system of entire data in order to efficiently extract the feature value of new user data at the stage of predicting and classifying a content polluter. And it comparatively assesses whether the proposed method maintains classification accuracy and improves time efficiency in comparison with batch processing method through experiment.
 Keywords
SNS;
 Language
Korean
 Cited by
 References
1.
Ito, J., Song, J., Toda, H., Koike, Y., and Oyama, S., "Assessment of Tweet Credibility with LDA Features", Proc. of the 24th International Conference on World Wide Web, pp.953-958, 2015.

2.
Jin Seop Han, Byung Joon Park, "Incremental Spammer Feature Extraction for the Spam Detection in Social Networking Service", International Journal of Applied Engineering Research, Vol. 10, No. 18, pp.39269-39273, 2015.

3.
Tsolmon, Bayar, and Kyung-Soon Lee, "An event extraction model based on timeline and user analysis in latent dirichlet allocation", Proc. of the 37th international ACM SIGIR conference on Research & development in information retrieval. ACM, pp.1187-1190, 2014.

4.
Sreedevi, M., Kumar, and G. V., "Parallel and Distributed Approach for Mining Closed Regular Patterns on Incremental Databases at User Thresholds", Proc. of the 2014 International Conference on Information and Communication Technology for Competitive Strategies, ACM, pp. 59-63, 2014.

5.
Chen, Y. C., Weng, J. T. Y., Wang, J. Z., Chou, C. L., Huang, J. L., and Lee, S. Y., "Incrementally Mining Temporal Patterns in Interval-based Databases", Data Science and Advanced Analytics (DSAA), pp. 304-311, 2014.

6.
Beutel, A., Xu, W., Guruswami, V., Palow, C., and Faloutsos, C., "CopyCatch: stopping group attacks by spotting lockstep behavior in social networks", Proc. of the 22nd international conference on World Wide Web, pp.119-130, 2013.

7.
Mehta, Gunjan, Deepa Sharma, and Ekta Chauhan, "Application of Incremental Mining and Apriori Algorithm on Library Transactional Database", International Journal of Computer Applications, pp. 73-78, 2013.

8.
Miray Kas, Matthew Wachs, Kathleen M. Carley, and L. Richard Carley, "Incremental Algorithm for Updating Betweenness Centrality in Dynamically Growing Networks", Proc. of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining. ACM, pp. 33-40, 2013.

9.
Miray Kas, Kathleen M. Carley, L. and Richard Carley, "Incremental Closeness Centrality for Dynamically Changing Social Networks", Advances in Social Networks Analysis and Mining (ASONAM), pp. 1250-1258, 2013.

10.
Jin Seop Han, Byung Joon Park, "Efficient Detection of Content Polluters in Social Networks", IT Convergence and Security 2012(LNEE), pp. 991-996, 2013.

11.
Shah, Siddharth, N. C. Chauhan, and S. D. Bhander, "Incremental Mining of Association Rules: A Survey", International Journal of Computer Science and Information Technologies, Vol. 3, no. 3, pp. 4071-4074, 2012.

12.
Papadopoulos, S., Kompatsiaris, Y., Vakali, A., and Spyridonos, P., "Community detection in Social Media Performance and application considerations", Data Mining and Knowledge Discovery 24.(3), pp. 515-554, 2012. crossref(new window)

13.
Hongyu Gao, Yan Chen, Kathy Lee, Diana Palsetia, and Alok Choudhary, "Towards Online Spam Filtering in Social Networks", Proc. of 19th Network Distributed System Security (NDSS) Symposium, http://www.internetsociety.org, 2012.

14.
Maarten Bosma, Edgar Meij, and Wouter Weerkamp, W., "A Framework for Unsupervised Spam Detection in Social Networking Sites", Proc. of European Conference on In-formation Retrieval (ECIR), pp. 364-375, 2012.

15.
Eibe Frank, Available from http://www.cs.waikato.ac.nz/ml/weka/downloading.html, 2012.

16.
Jonghyuk Song, Sangho Lee and Jong Kim, "Spam filtering in twitter using sender-receiver relationship", Proc. of the 14th International Symposium on Recent Advances in Intrusion Detection (RAID), pp. 301-317, 2011.

17.
KristoFer Beck, "Analyzing Tweets to Identify Malicious Messages", Proc. of Electro/Information Technology (EIT) IEEE International Conference, pp. 1-5, 2011.

18.
Kyumin Lee, Brian David Eoff, and James Caverlee, "Seven Months with the Devils: A Long-Term Study of Content Polluters on Twitter", Proc. of International AAAI Conference on Weblogs and Social Media (ICWSM), pp. 185-192, 2011.

19.
Saeed Abu-Nimeh, Thomas M. Chen, and Omar Alzubi, "Malicious and Spam Posts in Online Social Networks", IEEE Computer Society, Vol. 9, pp. 23-28, 2011.

20.
Muhammad Atif Qureshi, Tae-Seob Yun, Jeong-Hoon Lee, Kyu-Young Whang, "Improving the Quality of Web Spam Filtering by Using Seed Refinement", Journal of IEIE, CI, Vol. 48, no. 6, pp. 123-139, 2011.11.

21.
Seung-Hyun Seo, Taenam Cho, "Group Key Management Protocol for Secure Social Network Service", Journal of IEIE, CI, Vol. 48, no. 3, pp. 18-26, 2011.5.

22.
Chao Yang, Robert Chandler Harkreader, and Guofei Gu, "Die free or live hard? Empirical evaluation and new design for fighting evolving twitter spammers", Recent Advances in Intrusion Detection (RAID), Vol. 6961, pp.318-337, 2011.