Microblog User Location Inference Based on POI and Query Likelihood Model

Location inference of microblog users is of great significance for disaster monitoring, public opinion tracing and tracking, and extensive location-based services. However due to the noisy content of microblog text and the ambiguity of geographic location, it is quite difficult to infer user location based only on user-generated text. This paper proposes a microblog user location inference algorithm based on POI and query likelihood model, named PaQL. First, the POI (Point of Interest) model of each region is constructed based on the electronic map. Then, from the word segmentation results of the user’s blog texts, the POIs with stronger location orientation are extracted as user features. Next, the inverse region frequency of POIs is calculated, based on which the correlation between users and the candidate regions is calculated based on the query likelihood model. Finally, the candidate region with the highest correlation is considered as the user’s inferred location. The location inference experiment is conducted on the provincial-level data set (3,862k blogs of 154k users) and the city-level data set (3,086k blogs of 103k users) of Sina Weibo platform. The results show that: Compared with three existing typical algorithms, GP-FLIW, GP-LIWTF and WC-EFS, which are only based on user text, the precision of provincial-level inference is improved by 7.80%, 4.99% and 1.41%, respectively, and the city-level inference precision is improved by 10.67%, 8.38% and 3.72%, respectively. Moreover, the proposed algorithm also outperforms the existing methods in terms of recall and \({F}_{1}\).