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.
@inproceedings{DBLP:conf/adc/CuiZZS16,
author = {Lishan Cui and
Xiuzhen Zhang and
Xiangmin Zhou and
Flora D. Salim},
bibsource = {dblp computer science bibliography, https://dblp.org},
biburl = {https://dblp.org/rec/conf/adc/CuiZZS16.bib},
booktitle = {Databases Theory and Applications - 27th Australasian Database Conference,
ADC 2016, Sydney, NSW, Australia, September 28-29, 2016, Proceedings},
doi = {10.1007/978-3-319-46922-5_20},
editor = {Muhammad Aamir Cheema and
Wenjie Zhang and
Lijun Chang},
pages = {257--268},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
timestamp = {Mon, 16 Sep 2019 01:00:00 +0200},
title = {Topical Event Detection on Twitter},
url = {https://doi.org/10.1007/978-3-319-46922-5_20},
volume = {9877},
year = {2016}
}
© 2021 Flora Salim - CRUISE Research Group.
