On fine-grained geolocalisation of tweets and real-time traffic incident detection

Gonzalez Paule, J. D., Sun, Y. and Moshfeghi, Y. (2019) On fine-grained geolocalisation of tweets and real-time traffic incident detection. Information Processing and Management, 56(3), pp. 1119-1132. (doi:10.1016/j.ipm.2018.03.011)

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Abstract

Recently, geolocalisation of tweets has become important for a wide range of real-time applications, including real-time event detection, topic detection or disaster and emergency analysis. However, the number of relevant geotagged tweets available to enable such tasks remains insufficient. To overcome this limitation, predicting the location of non-geotagged tweets, while challenging, can increase the sample of geotagged data and has consequences for a wide range of applications. In this paper, we propose a location inference method that utilises a ranking approach combined with a majority voting of tweets, where each vote is weighted based on evidence gathered from the ranking. Using geotagged tweets from two cities, Chicago and New York (USA), our experimental results demonstrate that our method (statistically) significantly outperforms state-of-the-art baselines in terms of accuracy and error distance, in both cities, with the cost of decreased coverage. Finally, we investigated the applicability of our method in a real-time scenario by means of a traffic incident detection task. Our analysis shows that our fine-grained geolocalisation method can overcome the limitations of geotagged tweets and precisely map incident-related tweets at the real location of the incident.

Item Type:Articles
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Gonzalez Paule, Jorge and Moshfeghi, Dr Yashar and Sun, Mr Yeran
Authors: Gonzalez Paule, J. D., Sun, Y., and Moshfeghi, Y.
College/School:College of Science and Engineering > School of Computing Science
College of Social Sciences > School of Social and Political Sciences > Urban Studies
Journal Name:Information Processing and Management
Publisher:Elsevier
ISSN:0306-4573
ISSN (Online):1873-5371
Published Online:07 April 2018

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