Abstract
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.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
References
Abel, F., Hauff, C., Houben, G.-J., Stronkman, R., Tao, K.: Semantics + Filtering + Search = Twitcident. Exploring Information in Social Web Streams. In: Proceedings of the 23rd ACM Conference on Hypertext and Social Media, HT 2012 (2012)
Abel, F., Hauff, C., Houben, G.-J., Stronkman, R., Tao, K.: Twitcident: Fighting Fire with Information from Social Web Stream. In: Proceedings of the 23rd ACM Conference on Hypertext and Social Media, HT 2012 (2012)
Cataldi, M., Di Caro, L., Schifanella, C.: Emerging Topic Detection on Twitter Based on Temporal and Social Terms Evaluation. In: Proceedings of the Tenth International Workshop on Multimedia Data Mining, MDMKDD 2010 (2010)
Hall, M., Frank, E., Holmes, G., Pfahringer, P., Reutemann, B., Witten, I.H.: The WEKA Data Mining Software: An Update. ACM SIGKDD Explorations Newsletter 11(1), 10–18 (2009)
Hatcher, E., Gospodnetic, O., McCandless, M.: Lucene in Action. Manning. 2nd revised edn. (2010)
Li, R., Lei, K.H., Khadiwala, R., Chang, K.C.-C.: TEDAS: A Twitter-based Event Detection and Analysis System. In: Proceedings of the IEEE 28th International Conference on Data Engineering (2012)
Mathioudakis, M., Koudas, N.: TwitterMonitor: Trend Detection over the Twitter Stream. In: Proceedings of the 2010 ACM SIGMOD International Conference on Management of Data, SIGMOD 2010 (2010)
Nielsen, F.A.: A new ANEW: Evaluation of a word list for sentiment analysis in microblogs. In: Proceedings of the ESWC 2011 Workshop on Making Sense of Microposts (2011)
Petrovic, S., Osborne, M., Lavrenko, V.: Streaming first story detection with application to Twitter. In: Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics, NAACL-HLT 2010 (2010)
Petrovic, S., Osborne, M., Lavrenko, V.: Using paraphrases for improving first story detection in news and Twitter. In: Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2012 (2012)
Sakaki, T., Okazaki, M., Matsuo, Y.: Earthquake Shakes Twitter Users: Real-Time Event Detection by SocialSensors. In: Proceedings of the 19th International Conference on World Wide Web, WWW 2010 (2010)
Wilson, T., Wiebe, J., Hoffmann, P.: Recognizing Contextual Polarity in Phrase-Level Sentiment Analysis. In: Proceedings of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing, HLT 2005 (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Walther, M., Kaisser, M. (2013). Geo-spatial Event Detection in the Twitter Stream. In: Serdyukov, P., et al. Advances in Information Retrieval. ECIR 2013. Lecture Notes in Computer Science, vol 7814. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36973-5_30
Download citation
DOI: https://doi.org/10.1007/978-3-642-36973-5_30
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-36972-8
Online ISBN: 978-3-642-36973-5
eBook Packages: Computer ScienceComputer Science (R0)