Social Media as Sensor in Real World: Geolocate User with Microblog
| Autor: | Zhumin Chen, Kai Wu, Jun Ma, Xueqin Sui, Fengyu Zhou, Pengjie Ren |
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| Rok vydání: | 2014 |
| Předmět: |
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Information retrieval Computer science business.industry Microblogging Context (language use) computer.software_genre World Wide Web Geolocation Transfer (computing) Social media Artificial intelligence Baseline (configuration management) business Publication computer Natural language processing |
| Zdroj: | Communications in Computer and Information Science ISBN: 9783662459232 NLPCC |
| DOI: | 10.1007/978-3-662-45924-9_21 |
| Popis: | People always exist in the two dimensional space, i.e. time and space, in the real world. How to detect users’ locations automatically is significant for many location-based applications such as dietary recommendation and tourism planning. With the rapid development of social media such as Sina Weibo and Twitter, more and more people publish messages at any time which contain their real-time location information. This makes it possible to detect users’ locations automatically by social media. In this paper, we propose a method to detect a user’s city-level locations only based on his/her published posts in social media. Our approach considers two components: a Chinese location library and a model based on words distribution over locations. The former one is used to match whether there is a location name mentioned in the post. The latter one is utilized to mine the implied location information under the non-location words in the post. Furthermore, for a user’s detected location sequence, we consider the transfer speed between two adjacent locations to smooth the sequence in context. Experiments on real dataset from Sina Weibo demonstrate that our approach can outperform baseline methods significantly in terms of Precision, Recall and F1. |
| Databáze: | OpenAIRE |
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