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Title Language Modeling on Location-Based Social Networks
Authors Juglar Díaz, Felipe Bravo-Marquez, Bárbara Poblete
Publication date 2022
Abstract 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.
Pages article 147
Volume 11
Journal name ISPRS International Journal of Geo-Information
Publisher Molecular Diversity Preservation International (Basel, Switzerland)
Reference URL View reference page