Exceptional spatio-temporal behavior mining through Bayesian non-parametric modeling
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Exceptional spatio-temporal behavior mining through Bayesian non-parametric modeling Xin Du1
· Yulong Pei1 · Wouter Duivesteijn1 · Mykola Pechenizkiy1
Received: 17 September 2018 / Accepted: 13 January 2020 © The Author(s) 2020
Abstract Collective social media provides a vast amount of geo-tagged social posts, which contain various records on spatio-temporal behavior. Modeling spatio-temporal behavior on collective social media is an important task for applications like tourism recommendation, location prediction and urban planning. Properly accomplishing this task requires a model that allows for diverse behavioral patterns on each of the three aspects: spatial location, time, and text. In this paper, we address the following question: how to find representative subgroups of social posts, for which the spatio-temporal behavioral patterns are substantially different from the behavioral patterns in the whole dataset? Selection and evaluation are the two challenging problems for finding the exceptional subgroups. To address these problems, we propose BNPM: a Bayesian non-parametric model, to model spatio-temporal behavior and infer the exceptionality of social posts in subgroups. By training BNPM on a large amount of randomly sampled subgroups, we can get the global distribution of behavioral patterns. For each given subgroup of social posts, its posterior distribution can be inferred by BNPM. By comparing the posterior distribution with the global distribution, we can quantify the exceptionality of each given subgroup. The exceptionality scores are used to guide the search process within the exceptional model mining framework to automatically discover the exceptional subgroups. Various experiments are conducted to evaluate the effectiveness and efficiency of our method. On four real-world datasets our method discovers subgroups coinciding with events, subgroups distinguishing professionals from tourists, and subgroups whose consistent exceptionality can only be truly appreciated by combining exceptional spatio-temporal and exceptional textual behavior. Keywords Subgroup discovery · Exceptional model mining · Spatio-temporal analytics · Collective social media · Bayesian non-parametric model
Responsible editor: Karsten Borgwardt, Po-Ling Loh, Evimaria Terzi, Antti Ukkonen
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Xin Du [email protected]
Extended author information available on the last page of the article
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1 Introduction Popular social media platforms such as Twitter and Instagram have millions of users who share their photos, stories and geo-locations. This allows the collective social media to reflect diverse human behavioral patterns. The behavioral patterns in social posts are represented by distributions of spatial locations, time, and word topics (Hong et al. 2012). Specific deviations across any combination of these three distributions can indicate interesting, exceptional behavior of the population; one can for instance see such deviations surrounding large events, such as sports games and concerts (Zheng et al. 2018). In this
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