Abstract

The unprecedented use of social media through smartphones and other web-enabled mobile devices has enabled the rapid adoption of platforms like Twitter. Event detection has found many applications on the web, including breaking news identification and summarization. The recent increase in the usage of Twitter during crises has attracted researchers to focus on detecting events in tweets. However, current solutions have focused on static Twitter data. The necessity to detect events in a streaming environment during fast paced events such as a crisis presents new opportunities and challenges. In this paper, we investigate event detection in the context of real-time Twitter streams as observed in real-world crises. We highlight the key challenges in this problem: the informal nature of text, and the high-volume and high-velocity characteristics of Twitter streams. We present a novel approach to address these challenges using single-pass clustering and the compression distance to efficiently detect events in Twitter streams. Through experiments on large Twitter datasets, we demonstrate that the proposed framework is able to detect events in near real-time and can scale to large and noisy Twitter streams.

Original languageEnglish (US)
Title of host publicationProceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015
PublisherAssociation for Computing Machinery, Inc
Pages496-499
Number of pages4
ISBN (Print)9781450338547
DOIs
StatePublished - Aug 25 2015
EventIEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015 - Paris, France
Duration: Aug 25 2015Aug 28 2015

Other

OtherIEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015
CountryFrance
CityParis
Period8/25/158/28/15

Fingerprint

Smartphones
Mobile devices
Experiments

ASJC Scopus subject areas

  • Computer Science Applications
  • Computer Networks and Communications

Cite this

Kumar, S., Liu, H., Mehta, S., & Subramaniam, L. V. (2015). Exploring a scalable solution to identifying events in noisy Twitter streams. In Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015 (pp. 496-499). Association for Computing Machinery, Inc. https://doi.org/10.1145/2808797.2809389

Exploring a scalable solution to identifying events in noisy Twitter streams. / Kumar, Shamanth; Liu, Huan; Mehta, Sameep; Subramaniam, L. Venkata.

Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015. Association for Computing Machinery, Inc, 2015. p. 496-499.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Kumar, S, Liu, H, Mehta, S & Subramaniam, LV 2015, Exploring a scalable solution to identifying events in noisy Twitter streams. in Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015. Association for Computing Machinery, Inc, pp. 496-499, IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015, Paris, France, 8/25/15. https://doi.org/10.1145/2808797.2809389
Kumar S, Liu H, Mehta S, Subramaniam LV. Exploring a scalable solution to identifying events in noisy Twitter streams. In Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015. Association for Computing Machinery, Inc. 2015. p. 496-499 https://doi.org/10.1145/2808797.2809389
Kumar, Shamanth ; Liu, Huan ; Mehta, Sameep ; Subramaniam, L. Venkata. / Exploring a scalable solution to identifying events in noisy Twitter streams. Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015. Association for Computing Machinery, Inc, 2015. pp. 496-499
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