On-line relevant anomaly detection in the Twitter stream: An efficient bursty keyword detection model

Proceedings of the ACM SIGKDD Workshop on Outlier Detection and Description, ODD 2013

Volumn , Issue , 2013, Pages 31-39

  Guzman, Jheser ^a   Poblete, Barbara ^a  

^a UNIVERSITY OF CHILE   (Chile)

Author keywords

Bursty keyword detection; Text mining; Twitter

Indexed keywords

AVERAGE COMPLEXITY; EMERGING TOPIC DETECTIONS; INFORMATION CHANNELS; KEYWORD DETECTIONS; MICRO-BLOGGING PLATFORMS; ON-LINE SOCIAL NETWORKS; TEXT MINING; TWITTER;

DATA MINING; RESEARCH; STATISTICS;

SOCIAL NETWORKING (ONLINE);

EID: 84890621876     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2500853.2500860     Document Type: Conference Paper

References (26)

1. P. Adler and S. Kwon. Social capital: Prospects for a new concept. Academy of management review, pages 17-40, 2002.
2. D. Blei, A. Ng, and M. Jordan. Latent Dirichlet Allocation. The Journal of Machine Learning Research, 3:993-1022, 2003.
3. J. Bollen, H. Mao, and X. Zeng. Twitter mood predicts the stock market. Journal of Computational Science, 2011.
4. S. Gaito, M. Zignani, G. P. Rossi, A. Sala, X. Wang, H. Zheng, and B. Y. Zhao. On the Bursty Evolution of Online Social Networks. arXiv preprint arXiv:1203.6744, 2012.
5. K. Jones. A statistical interpretation of term specificity and its application in retrieval. Journal of documentation, 28(1):11-21, 1972.
6. S. Kellert. In the wake of chaos: Unpredictable order in dynamical systems. University of Chicago press, 1993.
7. K. Kireyev, L. Palen, and K. Anderson. Applications of topics models to analysis of disaster-related Twitter data. In NIPS Workshop on Applications for Topic Models: Text and Beyond, 2009.
8. J. Kleinberg. Bursty and hierarchical structure in streams. Data Mining and Knowledge Discovery, 7(4):373-397, 2003.
9. D. Knuth. The art ofcomputer programming, vol. 3: Sorting and Searching. Reading, MA: Addison-Wesley, 19:496-503, 1973.
10. H. Kwak, C. Lee, H. Park, and S. Moon. What is Twitter? a social network or a news media? In Proceedings of the 19th international conference on World Wide Web, pages 591-600. ACM, 2010.
11. V. Lampos, T. De Bie, and N. Cristianini. Flu detector-tracking epidemics on Twitter. Machine Learning and Knowledge Discovery in Databases, pages 599-602, 2010.
12. J. Leskovec, L. Backstrom, and J. Kleinberg. Meme-tracking and the dynamics of the news cycle. In Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 497-506. ACM, 2009.
13. C. Lin, B. Zhao, Q. Mei, and J. Han. Pet: A statistical model for popular events tracking in social communities. In Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 929-938. ACM, 2010.
14. M. Mathioudakis and N. Koudas. Twittermonitor: Trend detection over the Twitter stream. In Proceedings of the 2010 international conference on Management of data, pages 1155-1158. ACM, 2010.
15. M. Naaman, H. Becker, and L. Gravano. Hip and trendy: Characterizing emerging trends on Twitter. Journal of the American Society for Information Science and Technology, 2011.
16. X. Phan and C. Nguyen. Gibbslda++, a C/C++ Implementation of Latent Dirichlet Allocation (LDA) using Gibbs Sampling for parameter estimation and inference. http://gibbslda.sourceforge.net/.
17. T. Sakaki, M. Okazaki, and Y. Matsuo. Earthquake shakes Twitter users: real-time event detection by social sensors. In Proceedings of the 19th international conference on World wide web, pages 851-860. ACM, 2010.
18. H. Sayyadi, M. Hurst, and A. Maykov. Event detection and tracking in social streams. In Proceedings of International Conference on Weblogs and Social Media (ICWSM), 2009.
19. B. Sharifi, M. Hutton, and J. Kalita. Summarizing microblogs automatically. In Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics, pages 685-688. Association for Computational Linguistics, 2010.
20. K. Starbird, L. Palen, A. Hughes, and S. Vieweg. Chatter on the red: what hazards threat reveals about the social life of microblogged information. In Proceedings of the 2010 ACM conference on Computer supported cooperative work, pages 241-250. ACM, 2010.
21. R. Swan and J. Allan. Extracting significant time varying features from text. In Proceedings of the eighth international conference on Information and knowledge management, pages 38-45. ACM, 1999.
22. R. Swan and J. Allan. Automatic generation of overview timelines. In Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval, pages 49-56. ACM, 2000.
23. S. Vieweg, A. Hughes, K. Starbird, and L. Palen. Microblogging during two natural hazards events: What Twitter may contribute to situational awareness. In Proceedings of the 28th international conference on Human factors in computing systems, pages 1079-1088. ACM, 2010.
24. J. Weng, Y. Yao, E. Leonardi, and F. Lee. Event detection in Twitter. In Fifth International AAAI Conference on Weblogs and Social Media, 2011.
25. J. Yang and J. Leskovec. Patterns of temporal variation in online media. In Proceedings of the fourth ACM international conference on Web search and data mining, pages 177-186. ACM, 2011.
26. X. Zhang, H. Fuehres, and P. Gloor. Predicting stock market indicators through Twitter, I hope it is not as bad as I fear. Procedia-Social and Behavioral Sciences, 26:55-62, 2011.