Language Modeling on Location-Based Social Networks

ISPRS International Journal of Geo-Information

Juglar Diaz¹  Barbara Poblete¹  Felipe Bravo-Marquez¹ 

[1] Department of Computer Science, University of Chile & IMFD, Santiago 8370456, Chile;

关键词: spatio-temporal text data;    location-based social networks;    language models;   

DOI  :  10.3390/ijgi11020147

来源: DOAJ

【 摘 要 】

The popularity of mobile devices with GPS capabilities, along with the worldwide adoption of social media, have created a rich source of text data combined with spatio-temporal information. Text data collected from location-based social networks can be used to gain space–time insights into human behavior and provide a view of time and space from the social media lens. From a data modeling perspective, text, time, and space have different scales and representation approaches; hence, it is not trivial to jointly represent them in a unified model. Existing approaches do not capture the sequential structure present in texts or the patterns that drive how text is generated considering the spatio-temporal context at different levels of granularity. In this work, we present a neural language model architecture that allows us to represent time and space as context for text generation at different granularities. We define the task of modeling text, timestamps, and geo-coordinates as a spatio-temporal conditioned language model task. This task definition allows us to employ the same evaluation methodology used in language modeling, which is a traditional natural language processing task that considers the sequential structure of texts. We conduct experiments over two datasets collected from location-based social networks, Twitter and Foursquare. Our experimental results show that each dataset has particular patterns for language generation under spatio-temporal conditions at different granularities. In addition, we present qualitative analyses to show how the proposed model can be used to characterize urban places.

【 授权许可】

Unknown