A study of retrospective and on-line event detection
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Extended Boolean information retrieval
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Temporal summaries of new topics
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An Incremental Approach to Building a Cluster Hierarchy
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Bursty and Hierarchical Structure in Streams
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Event threading within news topics
Proceedings of the thirteenth ACM international conference on Information and knowledge management
Centroid-based summarization of multiple documents
Information Processing and Management: an International Journal
Summarizing email conversations with clue words
Proceedings of the 16th international conference on World Wide Web
Time-dependent event hierarchy construction
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Why we twitter: understanding microblogging usage and communities
Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 workshop on Web mining and social network analysis
TSCAN: a novel method for topic summarization and content anatomy
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Web Mining for Understanding Stories through Graph Visualisation
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
How and why people Twitter: the role that micro-blogging plays in informal communication at work
Proceedings of the ACM 2009 international conference on Supporting group work
Meme-tracking and the dynamics of the news cycle
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Chatter on the red: what hazards threat reveals about the social life of microblogged information
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Similarity measures for short segments of text
ECIR'07 Proceedings of the 29th European conference on IR research
What is Twitter, a social network or a news media?
Proceedings of the 19th international conference on World wide web
Earthquake shakes Twitter users: real-time event detection by social sensors
Proceedings of the 19th international conference on World wide web
Recommending twitter users to follow using content and collaborative filtering approaches
Proceedings of the fourth ACM conference on Recommender systems
You are where you tweet: a content-based approach to geo-locating twitter users
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Discovering global and local bursts in a stream of news
Proceedings of the 27th Annual ACM Symposium on Applied Computing
Hierarchical clustering in improving microblog stream summarization
CICLing'13 Proceedings of the 14th international conference on Computational Linguistics and Intelligent Text Processing - Volume 2
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Outline social media networks (OSMNs) such as Twitter provide great opportunities for public engagement and event information dissemination. Event-related discussions occur in real time and at the worldwide scale. However, these discussions are in the form of short, unstructured messages and dynamically woven into daily chats and status updates. Compared with traditional news articles, the rich and diverse user-generated content raises unique new challenges for tracking and analyzing events. Effective and efficient event modeling is thus essential for real-time information-intensive OSMNs. In this work, we propose ETree, an effective and efficient event modeling solution for social media network sites. Targeting the unique challenges of this problem, ETree consists of three key components: (1) an n-gram based content analysis technique for identifying core information blocks from a large number of short messages, (2) an incremental and hierarchical modeling technique for identifying and constructing event theme structures at different granularities, and (3) an enhanced temporal analysis technique for identifying inherent causalities between information blocks. Detailed evaluation using 3.5 million tweets over a 5-month period demonstrates that ETree can efficiently generate high-quality event structures and identify inherent causal relationships with high accuracy.