A Microblogs Location Inferring Method Based on Hybrid Language Model

Abstract

Inferring tweets’ location has emerged to be a critical and interesting issue in social media eld. Nowdays, it is still a challenging problem to infer the location based on the text context, meanwhile the inference granularity is too rough. This paper proposed a location inferring model for text context based on hybrid language model in district granularity. Using tweets that locations are already known as seeds to build n-gram language model through analyzing the geo-tag features on the tweets from Sina-Weibo platform. Then the tweets’ location will be inferred based on the language model. The f-measure district level of our method is to 39.7% on a Sina-Weibo test set of 9,999 tweets from 8,273 users; compared with the method based on unigram language model, the proposed method can achieve better accuracy.