Year: 2015
Type: article
Abstract: Geo-location data from social media offers us information, in new ways, to understand people's attitudes and interests through their activity choices. In this paper, we explore the idea of inferring individual life-style patterns from activity-location choices revealed in social media. We present a model to understand life-style patterns using the contextual information (e. g. location categories) of user check-ins. Probabilistic topic models are developed to infer individual geo life-style patterns from two perspectives: i) to characterize the patterns of user interests to different types of places and ii) to characterize the patterns of user visits to different neighborhoods. The method is applied to a dataset of Foursquare check-ins of the users from New York City. The co-existence of several location contexts and the corresponding probabilities in a given pattern provide useful information about user interests and choices. It is found that geo life-style patterns have similar items-either nearby neighborhoods or similar location categories. The semantic and geographic proximity of the items in a pattern reflects the hidden regularity in user preferences and location choice behavior.
Source: PLoS ONE
Authors: Samiul Hasan, Satish V. Ukkusuri
Institutions: CSIRO Land and Water, Purdue University West Lafayette
Language: English
Subfield: Transportation
Field: Social Sciences
Domain: Social Sciences
Open Access status: gold
Estimated APC paid: $2,382
OpenAlex ID: W401548239