Recognizing contextual polarity in phrase-level sentiment analysis
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
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
Streaming first story detection with application to Twitter
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Semantics + filtering + search = twitcident. exploring information in social web streams
Proceedings of the 23rd ACM conference on Hypertext and social media
TEDAS: A Twitter-based Event Detection and Analysis System
ICDE '12 Proceedings of the 2012 IEEE 28th International Conference on Data Engineering
Using paraphrases for improving first story detection in news and Twitter
NAACL HLT '12 Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Robust detection of hyper-local events from geotagged social media data
Proceedings of the Thirteenth International Workshop on Multimedia Data Mining
The influence of social norms on synchronous versus asynchronous communication technologies
Proceedings of the 1st ACM international workshop on Personal data meets distributed multimedia
Landmark-based user location inference in social media
Proceedings of the first ACM conference on Online social networks
Evidential location estimation for events detected in Twitter
Proceedings of the 7th Workshop on Geographic Information Retrieval
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The rise of Social Media services in the last years has created huge streams of information that can be very valuable in a variety of scenarios. What precisely these scenarios are and how the data streams can efficiently be analyzed for each scenario is still largely unclear at this point in time and has therefore created significant interest in industry and academia. In this paper, we describe a novel algorithm for geo-spatial event detection on Social Media streams. We monitor all posts on Twitter issued in a given geographic region and identify places that show a high amount of activity. In a second processing step, we analyze the resulting spatio-temporal clusters of posts with a Machine Learning component in order to detect whether they constitute real-world events or not. We show that this can be done with high precision and recall. The detected events are finally displayed to a user on a map, at the location where they happen and while they happen.