On-line new event detection and tracking
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Event detection from time series data
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Learning Approaches for Detecting and Tracking News Events
IEEE Intelligent Systems
Bursty and hierarchical structure in streams
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
A System for new event detection
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Text classification and named entities for new event detection
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Parameter free bursty events detection in text streams
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Analyzing feature trajectories for event detection
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Spatio-Temporal-Thematic Analysis of Citizen Sensor Data: Challenges and Experiences
WISE '09 Proceedings of the 10th International Conference on Web Information Systems Engineering
Earthquake shakes Twitter users: real-time event detection by social sensors
Proceedings of the 19th international conference on World wide web
Extracting events and event descriptions from Twitter
Proceedings of the 20th international conference companion on World wide web
TEDAS: A Twitter-based Event Detection and Analysis System
ICDE '12 Proceedings of the 2012 IEEE 28th International Conference on Data Engineering
Open domain event extraction from twitter
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Twevent: segment-based event detection from tweets
Proceedings of the 21st ACM international conference on Information and knowledge management
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Social media sites such as Twitter and Facebook have emerged as popular tools for people to express their opinions on various topics. The large amount of data provided by these media is extremely valuable for mining trending topics and events. In this paper, we build an efficient, scalable system to detect events from tweets (ET). Our approach detects events by exploring their textual and temporal components. ET does not require any target entity or domain knowledge to be specified; it automatically detects events from a set of tweets. The key components of ET are (1) an extraction scheme for event representative keywords, (2) an efficient storage mechanism to store their appearance patterns, and (3) a hierarchical clustering technique based on the common co-occurring features of keywords. The events are determined through the hierarchical clustering process. We evaluate our system on two data-sets; one is provided by VAST challenge 2011, and the other published by US based users in January 2013. Our results show that we are able to detect events of relevance efficiently.