Undoubtedly, Location-based Social Networks (LBSNs) provide an interesting source of geo-located data that we have previously used to obtain patterns of the dynamics of crowds throughout urban areas. According to our previous results, activity in LBSNs reflects the real activity in the city. Therefore, unexpected behaviors in the social media activity are a trustful evidence of unexpected changes of the activity in the city. In this paper we introduce a hybrid solution to early detect these changes based on applying a combination of two approaches, the use of entropy analysis and clustering techniques, on the data gathered from LBSNs. In particular, we have performed our experiments over a data set collected from Instagram for seven months in New York City, obtaining promising results.

翻译：毫无疑问，基于位置的社交网络（Location-based Social Networks，LBSNs）提供了有趣的地理定位数据源，我们此前已利用这些数据获取城市区域人群动态模式。根据先前研究结果，LBSN中的活动能够反映城市中的真实活动。因此，社交媒体活动中的异常行为是城市活动发生意外变化的可靠证据。本文提出一种混合解决方案，通过结合熵分析与聚类技术两种方法，对从LBSN收集的数据进行早期异常检测。具体而言，我们基于从Instagram收集的纽约市七个月数据集进行了实验，获得了令人鼓舞的结果。