TwitterNews+: A framework for real time event detection from the twitter data stream

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionResearchpeer-review

Abstract

In recent years, substantial research efforts have gone into investigating different approaches to the detection of events in real time from the Twitter data stream. Most of these approaches, however, suffer from a high computational cost and are not evaluated using a publicly available corpus, thus making it difficult to properly compare them. In this paper, we propose a scalable event detection system, TwitterNews+, to detect and track newsworthy events in real time. TwitterNews+ uses a novel approach to cluster event related tweets from Twitter with a significantly lower computational cost compared to the existing state-of-theart approaches. Finally, we evaluate the effectiveness of TwitterNews+ using a publicly available corpus and its associated ground truth data set of newsworthy events. The result of the evaluation shows a significant improvement, in terms of recall and precision, over the baselines we have used.

LanguageEnglish
Title of host publicationSocial Informatics - 8th International Conference, SocInfo 2016, Proceedings
EditorsEmma Spiro, Yong-Yeol Ahn
Place of PublicationCham
PublisherSpringer, Springer Nature
Pages224-239
Number of pages16
Volume10046 LNCS
ISBN (Print)9783319478791
DOIs
Publication statusPublished - 2016
Event8th International Conference on Social Informatics, SocInfo 2016 - Bellevue, United States
Duration: 11 Nov 201614 Nov 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10046 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other8th International Conference on Social Informatics, SocInfo 2016
CountryUnited States
CityBellevue
Period11/11/1614/11/16

Fingerprint

Event Detection
Data Streams
Computational Cost
Costs
Baseline
Framework
Evaluate
Evaluation
Corpus

Keywords

  • Event detection
  • Incremental clustering
  • Microblog
  • Social media
  • Twitter

Cite this

Hasan, M., Orgun, M. A., & Schwitter, R. (2016). TwitterNews+: A framework for real time event detection from the twitter data stream. In E. Spiro, & Y-Y. Ahn (Eds.), Social Informatics - 8th International Conference, SocInfo 2016, Proceedings (Vol. 10046 LNCS, pp. 224-239). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10046 LNCS). Cham: Springer, Springer Nature. https://doi.org/10.1007/978-3-319-47880-7_14
Hasan, Mahmud ; Orgun, Mehmet A. ; Schwitter, Rolf. / TwitterNews+ : A framework for real time event detection from the twitter data stream. Social Informatics - 8th International Conference, SocInfo 2016, Proceedings. editor / Emma Spiro ; Yong-Yeol Ahn. Vol. 10046 LNCS Cham : Springer, Springer Nature, 2016. pp. 224-239 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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abstract = "In recent years, substantial research efforts have gone into investigating different approaches to the detection of events in real time from the Twitter data stream. Most of these approaches, however, suffer from a high computational cost and are not evaluated using a publicly available corpus, thus making it difficult to properly compare them. In this paper, we propose a scalable event detection system, TwitterNews+, to detect and track newsworthy events in real time. TwitterNews+ uses a novel approach to cluster event related tweets from Twitter with a significantly lower computational cost compared to the existing state-of-theart approaches. Finally, we evaluate the effectiveness of TwitterNews+ using a publicly available corpus and its associated ground truth data set of newsworthy events. The result of the evaluation shows a significant improvement, in terms of recall and precision, over the baselines we have used.",
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Hasan, M, Orgun, MA & Schwitter, R 2016, TwitterNews+: A framework for real time event detection from the twitter data stream. in E Spiro & Y-Y Ahn (eds), Social Informatics - 8th International Conference, SocInfo 2016, Proceedings. vol. 10046 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10046 LNCS, Springer, Springer Nature, Cham, pp. 224-239, 8th International Conference on Social Informatics, SocInfo 2016, Bellevue, United States, 11/11/16. https://doi.org/10.1007/978-3-319-47880-7_14

TwitterNews+ : A framework for real time event detection from the twitter data stream. / Hasan, Mahmud; Orgun, Mehmet A.; Schwitter, Rolf.

Social Informatics - 8th International Conference, SocInfo 2016, Proceedings. ed. / Emma Spiro; Yong-Yeol Ahn. Vol. 10046 LNCS Cham : Springer, Springer Nature, 2016. p. 224-239 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10046 LNCS).

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionResearchpeer-review

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Hasan M, Orgun MA, Schwitter R. TwitterNews+: A framework for real time event detection from the twitter data stream. In Spiro E, Ahn Y-Y, editors, Social Informatics - 8th International Conference, SocInfo 2016, Proceedings. Vol. 10046 LNCS. Cham: Springer, Springer Nature. 2016. p. 224-239. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-47880-7_14