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Topical Event Detection on Twitter

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Název: Topical Event Detection on Twitter
Autoři: Jenny Zhang, Flora Salim
Rok vydání: 2020
Témata: Other information and computing sciences not elsewhere classified, topical event detection, Burst detection, Database Management, Dynamic topic modelling, Topic mutation, Information and Computing Sciences not elsewhere classified
Popis: Event detection on Twitter has attracted active research. Although existing work considers the semantic topic structure of documents for event detection, the topic dynamics and the semantic consistency are under-investigated. In this paper, we study the problem of topical event detection in tweet streams. We define topical events as the bursty occurrences of semantically consistent topics. We decompose the problem of topical event detection into two components: (1) We address the issue of the semantic incoherence of the evolution of topics. We propose to improve topic modelling to filter out semantically inconsistent dynamic topics. (2) We propose to perform burst detection on the time series of dynamic topics to detect bursty occurrences. We apply our proposed techniques to the real world application by detecting topical events in public transport tweets. Experiments demonstrate that our approach can detect the newsworthy events with high success rate. Provided link supports the dataset used for this paper.
Druh dokumentu: dataset
Jazyk: unknown
Relation: https://figshare.com/articles/dataset/Topical_Event_Detection_on_Twitter/13121852
DOI: 10.25439/rmt.13121852.v2
Dostupnost: https://doi.org/10.25439/rmt.13121852.v2
https://figshare.com/articles/dataset/Topical_Event_Detection_on_Twitter/13121852
Rights: CC BY-NC 4.0
Přístupové číslo: edsbas.E6EC78CD
Databáze: BASE
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  Data: Topical Event Detection on Twitter
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  Data: Event detection on Twitter has attracted active research. Although existing work considers the semantic topic structure of documents for event detection, the topic dynamics and the semantic consistency are under-investigated. In this paper, we study the problem of topical event detection in tweet streams. We define topical events as the bursty occurrences of semantically consistent topics. We decompose the problem of topical event detection into two components: (1) We address the issue of the semantic incoherence of the evolution of topics. We propose to improve topic modelling to filter out semantically inconsistent dynamic topics. (2) We propose to perform burst detection on the time series of dynamic topics to detect bursty occurrences. We apply our proposed techniques to the real world application by detecting topical events in public transport tweets. Experiments demonstrate that our approach can detect the newsworthy events with high success rate. Provided link supports the dataset used for this paper.
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