Object matching in tweets with spatial models.
WSDM(2012)
摘要
ABSTRACTDespite their 140-character limitation, tweets embody a lot of valuable information, especially temporal and spatial. In this paper we study the geographic aspects of tweets, for a given object domain. We propose a user-level model for spatial encoding in tweets that goes beyond the explicit geo-coding or place name mentions; this model can be used to match objects to tweets. We illustrate our model and methodology using restaurants as the objects, and show a significant improvement in performance over using standard language models. En route, we obtain a method to geolocate users who tweet about geolocated objects; this may be of independent interest.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络