Probabilistic author-topic models for information discovery
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
NLTK: the Natural Language Toolkit
ETMTNLP '02 Proceedings of the ACL-02 Workshop on Effective tools and methodologies for teaching natural language processing and computational linguistics - Volume 1
Cost-effective outbreak detection in networks
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Labeled LDA: a supervised topic model for credit attribution in multi-labeled corpora
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1 - Volume 1
Multi-document summarization via budgeted maximization of submodular functions
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Comparing twitter and traditional media using topic models
ECIR'11 Proceedings of the 33rd European conference on Advances in information retrieval
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Microblogging is a form of blogging where posts typically consist of short content such as quick comments, phrases, URLs, or media, like images and videos. Because of the fast and compact nature of microblogs, users have adopted them for novel purposes, including sharing personal updates, spreading breaking news, promoting political views, marketing and tracking real time events. Thus, finding relevant information sources out of the rapidly growing content is an essential task. In this paper, we study the problem of understanding and analysing microblogs. We present a novel 2-stage framework to find potentially relevant content by extracting topics from the tweets and by taking advantage of submodularity.