Abstract
Increased popularity of social media sites such as Twitter, Facebook, Flickr, etc. have produced an enormous amount of spatio-temporal data. One of the application of this type of data is event detection. Most of event detection techniques have focused on temporal feature of data for detecting an event. However, location associated with data has to be taken into consideration to detect locality based event (local event) such as local festival, sporting event or emergency situations. Users in proximity of the location of an event are more likely to post messages about an event compared to users distant from the location of that event. In this paper, we are proposing a framework, called EventStory. Our framework first identifies locally significant key-words (LSK) by monitoring changes in the bursty nature of keywords in both local and global regions. Candidate event clusters are created based on co-occurrence of locally significant keywords (LSK) in the each keyword cluster. A cluster scoring scheme is used which uses the features of cluster to filter irrelevant clusters. A case study is presented to show effectiveness of our approach.
Keywords
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Becker, H., Naaman, M., Gravano, L.: Beyond Trending Topics: Real-World Event Identification on Twitter. In: International AAAI Conference on Weblogs and Social Media, Barcelona, Spain (July 2011)
Li, C., Sun, A., Datta, A.: Twevent: segment-based event detection from tweets. In: Proceedings of the 21st ACM International Conference on Information and Knowledge Management (CIKM 2012), pp. 155–164. ACM, New York (2012)
Chen, L., Roy, A.: Event detection from Flickr data through wavelet-based spatial analysis. In: Proceedings of the 18th ACM International Conference on Information and Knowledge Management, pp. 523–532 (2009)
Watanabe, K., Ochi, M., Okabe, M., Onai, R.: Jasmine: a real-time local-event detection system based on geolocation information propagated to microblogs. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management, CIKM 2011, pp. 2541–2544. ACM, New York (2011)
Weiler, A., Scholl, M.H., Wanner, F., Rohrdantz, C.: Event identification for local areas using social media streaming data. In: Proceedings of the ACM SIGMOD Workshop on Databases and Social Networks (DBSocial 2013), pp. 1–6. ACM, New York (2013)
Abdelhaq, H., Sengstock, C., Gertz, M.: EvenTweet: online localized event detection from twitter. Proceedings of the VLDB Endowment 6(12), 1326–1329 (2013)
Parikh, R., Karlapalem, K.: Et: events from tweets. In: Proceedings of the 22nd International Conference on World Wide Web Companion, pp. 613–620. International World Wide Web Conferences Steering Committee (2013)
Lappas, T., Vieira, M.R., Gunopulos, D., Tsortas, V.J.: On the spatiotemporal burstiness of terms. Proceedings of the VLDB Endowment, 826–847 (2012)
Abdelhaq, H., Gertz, M., Sengstock, C.: Spatio-temporal characteristics of bursty words in Twitter streams. In: Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, pp. 194–203. ACM (2013)
Abdi, H.: Z-scores. Encyclopedia of measurement and statistics. Sage, Thousand Oaks (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Bisht, S., Toshniwal, D. (2014). EventStory: Event Detection Using Twitter Stream Based on Locality. In: Corchado, E., Lozano, J.A., Quintián, H., Yin, H. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2014. IDEAL 2014. Lecture Notes in Computer Science, vol 8669. Springer, Cham. https://doi.org/10.1007/978-3-319-10840-7_48
Download citation
DOI: https://doi.org/10.1007/978-3-319-10840-7_48
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-10839-1
Online ISBN: 978-3-319-10840-7
eBook Packages: Computer ScienceComputer Science (R0)