Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1007/978-3-319-46922-5_20 | DATABASES THEORY AND APPLICATIONS, (ADC 2016) |
Keywords | Field | DocType |
Dynamic topic modelling, Topic mutation, Event detection, Burst detection | Data mining,Topic structure,Computer science,Semantic consistency,Topic model | Conference |
Volume | ISSN | Citations |
9877 | 0302-9743 | 1 |
PageRank | References | Authors |
0.37 | 11 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lishan Cui | 1 | 4 | 1.77 |
| Xiuzhen Zhang | 2 | 553 | 47.04 |
| Xiangmin Zhou | 3 | 319 | 25.53 |
| f salim | 4 | 40 | 10.93 |