| Title |
Real world city event extraction from Twitter data streams |
| ID_Doc |
43253 |
| Authors |
Zhou, YC; De, S; Moessner, K |
| Title |
Real world city event extraction from Twitter data streams |
| Year |
2016 |
| Published |
|
| DOI |
10.1016/j.procs.2016.09.069 |
| Abstract |
The immediacy of social media messages means that it can act as a rich and timely source of real world event information. The detected events can provide a context to observations made by other city information sources such as fixed sensor installations and contribute to building 'city intelligence'. In this work, we propose a novel unsupervised method to extract real world events that may impact city services such as traffic, public transport, public safety etc., from Twitter streams. We also develop a named entity recognition model to obtain the precise location of the related events and provide a qualitative estimation of the impact of the detected events. We apply our developed approach to a real world dataset of tweets collected from the city of London. (C) 2015 The Authors. Published by Elsevier B.V. |
| Author Keywords |
Smart city; Twitter; city events; event extraction |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Conference Proceedings Citation Index - Science (CPCI-S) |
| EID |
WOS:000387551200061 |
| WoS Category |
Computer Science, Information Systems; Computer Science, Theory & Methods; Medical Informatics; Telecommunications |
| Research Area |
Computer Science; Medical Informatics; Telecommunications |
| PDF |
https://doi.org/10.1016/j.procs.2016.09.069
|