标题:Social media as sensor in real world: Geolocate user with microblog
作者:Sui, Xueqin ;Chen, Zhumin ;Wu, Kai ;Ren, Pengjie ;Ma, Jun ;Zhou, Fengyu
通讯作者:Chen, Zhumin
作者机构:[Sui, Xueqin ;Chen, Zhumin ;Wu, Kai ;Ren, Pengjie ;Ma, Jun ] School of Computer Science and Technology, Shandong University, Jinan; 250101, China;[Zho 更多
会议名称:3rd CCF Conference on Natural Language Processing and Chinese Computing, NLPCC 2014
会议日期:5 December 2014 through 9 December 2014
来源:Communications in Computer and Information Science
出版年:2014
卷:496
页码:229-237
关键词:Location Detection; Social Media; Words Distribution over Locations
摘要: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 SinaWeibo demonstrate that our approach can outperform baseline methods significantly in terms of Precision, Recall and F1. © Springer-Verlag Berlin Heidelberg 2014.
收录类别:EI;SCOPUS
Scopus被引频次:3
资源类型:会议论文;期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-84916228472&partnerID=40&md5=3cacb8059bd2f0a1730ef5271d0b462f
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