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.

中文翻译：

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通过贝叶斯非参数建模进行异常时空行为挖掘

集体社交媒体提供了大量带有地理标签的社交帖子，其中包含有关时空行为的各种记录。在集体社交媒体上对时空行为进行建模是旅游推荐，位置预测和城市规划等应用程序中的重要任务。正确完成此任务需要一个模型，该模型需要在三个方面中的每个方面都具有不同的行为模式：空间位置，时间和文本。在本文中，我们解决了以下问题：如何找到具有代表性的社会职位子组，其时空行为模式与整个数据集中的行为模式明显不同？选择和评估是寻找优秀亚组的两个具有挑战性的问题。为了解决这些问题，我们建议BNPM：贝叶斯非参数模型，用于建模时空行为并推断小组中社交职位的特殊性。通过在大量随机采样的子组上训练BNPM，我们可以获得行为模式的全局分布。对于每个给定的社会职位子组，BNPM可以推断出其后验分布。通过比较后验分布和全局分布，我们可以量化每个给定子组的例外情况。例外得分用于指导例外模型挖掘框架内的搜索过程，以自动发现例外子组。进行各种实验以评估我们方法的有效性和效率。在四个真实的数据集上，我们的方法发现了与事件一致的子组，