A study of retrospective and on-line event detection
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
On-line new event detection and tracking
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Text Classification from Labeled and Unlabeled Documents using EM
Machine Learning - Special issue on information retrieval
Transductive Inference for Text Classification using Support Vector Machines
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
The Journal of Machine Learning Research
ICML '06 Proceedings of the 23rd international conference on Machine learning
Topics over time: a non-Markov continuous-time model of topical trends
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Analyzing feature trajectories for event detection
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Learning similarity metrics for event identification in social media
Proceedings of the third ACM international conference on Web search and data mining
Earthquake shakes Twitter users: real-time event detection by social sensors
Proceedings of the 19th international conference on World wide web
TwitterMonitor: trend detection over the twitter stream
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Emerging topic detection on Twitter based on temporal and social terms evaluation
Proceedings of the Tenth International Workshop on Multimedia Data Mining
Detecting controversial events from twitter
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Mining named entities with temporally correlated bursts from multilingual web news streams
Proceedings of the fourth ACM international conference on Web search and data mining
Identifying breakpoints in public opinion
Proceedings of the First Workshop on Social Media Analytics
Empirical study of topic modeling in Twitter
Proceedings of the First Workshop on Social Media Analytics
Emerging topic detection using dictionary learning
Proceedings of the 20th ACM international conference on Information and knowledge management
Proceedings of the fifth ACM international conference on Web search and data mining
Learning causality for news events prediction
Proceedings of the 21st international conference on World Wide Web
TM-LDA: efficient online modeling of latent topic transitions in social media
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Social TV analytics: a novel paradigm to transform TV watching experience
Proceedings of the 5th ACM Multimedia Systems Conference
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Microblog services have emerged as an essential way to strengthen the communications among individuals and organizations. These services promote timely and active discussions and comments towards products, markets as well as public events, and have attracted a lot of attentions from organizations. In particular, emerging topics are of immediate concerns to organizations since they signal current concerns of, and feedback by their users. Two challenges must be tackled for effective emerging topic detection. One is the problem of real-time relevant data collection and the other is the ability to model the emerging characteristics of detected topics and identify them before they become hot topics. To tackle these challenges, we first design a novel scheme to crawl the relevant messages related to the designated organization by monitoring multi-aspects of microblog content, including users, the evolving keywords and their temporal sequence. We then develop an incremental clustering framework to detect new topics, and employ a range of content and temporal features to help in promptly detecting hot emerging topics. Extensive evaluations on a representative real-world dataset based on Twitter data demonstrate that our scheme is able to characterize emerging topics well and detect them before they become hot topics.