On fine-grained geolocalisation of tweets and real-time traffic incident detection |
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| Institution: | 1. School of Computing Science, University of Glasgow, UK;2. Urban Big Data Centre, University of Glasgow, UK;3. Department of Computer & Information Sciences, University of Strathclyde, UK;1. Department of Computer Engineering, Eskisehir Technical University, Eskisehir 26555, Turkey;2. Computer Research and Development Center, Anadolu University, Eskisehir 26470, Turkey;1. School of Computer Science, South China Normal University, Guangzhou, China;2. Department of SEEM, The Chinese University of Hong Kong, Hong Kong;3. Graduate School at Shenzhen, Tsinghua University, Shenzhen, China;4. Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China;5. School of Electronics and Computer Engineering, Peking University Shenzhen Graduate School, China;1. Indian Statistical Institute, Kolkata, India;2. IBM Research, Dublin, Ireland;3. Dublin City University, Dublin, Ireland;1. Qatar Computing Research Institute, HBKU, Doha, Qatar;2. MIT Computer Science and Artificial Intelligence Laboratory, Cambridge, MA, USA;1. School of Data and Computer Science, Sun Yat-sen University, Guangzhou, China;2. School of Computer Science, University of Adelaide, Adelaide, Australia |
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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. |
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