Who will be participating next? Predicting the participation of dark web community

Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Volumn , Issue , 2012, Pages

  Tang, Xuning ^a   Yang, Christopher C ^a   Zhang, Mi ^a  

^a DREXEL UNIVERSITY   (United States)

Author keywords

prediction; ranking; topic and trend modeling; user interest

Indexed keywords

BIPARTITE GRAPHS; BROAD APPLICATION; DARK WEB; DATA SETS; LIMITED INFORMATION; NEW THREAD; POTENTIAL USERS; RANKING; SECURITY AGENTS; TOPIC DETECTION; USER INTERESTS; USERS' INTERESTS;

INFORMATION SCIENCE; WORLD WIDE WEB;

FORECASTING;

EID: 84864998665     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2331791.2331792     Document Type: Conference Paper

References (14)

1. BLEI, D.M., NG, A.Y., and JORDAN, M.I., 2003. Latent Dirichlet Allocation. The Journal of Machine Learning Research 3(Mar. 2003), 993-1022. DOI= http://dx.doi.org/10.1162/jmlr.2003.3.4-5.993.
2. BREESE, J.S., HECKERMAN, D., and KADIE, C., 1998. Empirical analysis of predictive algorithms for collaborative filtering. In Proceedings of the the Fourteenth conference on Uncertainty in artificial intelligence (Madison, WI, July 24-26 1998), Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 43-52.
3. CHOUDHURY, M.D. and SUNDARAM, H., 2011. Why do we converse on social media?: an analysis of intrinsic and extrinsic network factors. In Proceedings of the the 3rd ACM SIGMM international workshop on Social media (Scottsdale, AZ, USA, November 28 - December 01 2011), ACM, New York, NY, USA, 53-58. DOI= http://dx.doi.org/10.1145/2072609.2072625.
4. CHOUDHURY, M.D., SUNDARAM, H., JOHN, A., and SELIGMANN, D.D., 2009. What makes conversations interesting?: themes, participants and consequences of conversations in online social media. In Proceedings of the the 18th international conference on World wide web (Madrid, Spain, April 20-24 2009), ACM, New York, NY, USA, 331-340. DOI= http://dx.doi.org/10.1145/1526709.1526754.
5. FUNG, Y.-H., LI, C.-H., and CHEUNG, W.K., 2007 Online Discussion Participation Prediction Using Non-negative Matrix Factorization. In Proceedings of the the 2007 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Workshops (Silicon Valley, California, USA, November 2-5 2007 ), IEEE Computer Society, Washington, DC, USA, 284-287. DOI= http://dx.doi.org/10.1109/WI-IATW.2007.128.
6. GRUJIĆ, J., 2008. Movies Recommendation Networks as Bipartite Graphs. In Proceedings of the 8th International Conference on Computer Science (Kraków, Poland, June 23-25 2008), Springer, Verlag Berlin, Heidelberg, 576-583. DOI= http://dx.doi.org/10.1007/978-3-540-69389-5.
7. HAMERS, L., HEMERYCK, Y., HERWEYERS, G., JANSSEN, M., KETERS, H., ROUSSEAU, R., and VANHOUTTE, A., 1989. Similarity measures in scientometric research: the Jaccard index versus Salton's cosine formula. Information Processing and Management: an International Journal 25, 3 (May 1989), 315-318. DOI= http://dx.doi.org/10.1016/0306-4573(89)90048-4.
8. HUANG, Z., 2007. Selectively acquiring ratings for product recommendation. In Proceedings of the the ninth international conference on Electronic commerce (Minneapolis, MN, USA, August 19-22 2007), ACM, New York, NY, USA, 379-388. DOI= http://dx.doi.org/10.1145/1282100.1282171.
9. HUANG, Z., LI, X., and CHEN, H., 2005. Link prediction approach to collaborative filtering. In Proceedings of the Joint Conference on Digital Libraries 2005 (Denver, CO, USA, June 07-11 2005), ACM, New York, NY, USA, 141-142. DOI= http://dx.doi.org/10.1145/1065385.1065415.
10. KAWAMAE, N., 2011. Trend analysis model: trend consists of temporal words, topics, and timestamps. In Proceedings of the the fourth ACM international conference on Web search and data mining (Kowloon, Hong Kong, February 09-12 2011), ACM, New York, NY, USA, 317-326. DOI= http://dx.doi.org/ 10.1145/1935826.1935880.
11. LIU, J.-G., ZHOU, T., WANG, B.-H., ZHANG, Y.-C., and GUO, Q., 2010. Degree correlation of bipartite network on personalized recommendation. International Journal of Modern Physics C 21, 1, 137-147. DOI= http://dx.doi.org/10.1142/S0129183110014999.
12. LIU, R.-R., LIU, J.-G., JIA, C.-X., and WANG, B.-H., 2010. Personal recommendation via unequal resource allocation on bipartite networks Physica A: Statistical Mechanics and its Applications 389, 16, 3282-3289. DOI= http://dx.doi.org/http://dx.doi.org/10.1016/j.physa.2010.04.004.
13. RESNICK, P. and VARIAN, H.R., 1997. recommender systems. Communications of the ACM 40, 3 (Mar. 1997), 56-58. DOI= http://dx.doi.org/10.1145/245108. 245121.
14. YANO, T., COHEN, W.W., and SMITH, N.A., 2009. Predicting response to political blog posts with topic models. In Proceedings of the Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics (Boulder, Colorado, May 31-June 5 2009), Association for Computational Linguistics, Stroudsburg, PA, USA, 477-485.