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.

毫无疑问，基于位置的社交网络（LBSN）提供了有趣的地理位置数据源，我们以前曾使用该数据来获得整个市区人群动态的模式。根据我们之前的结果，LBSN中的活动反映了城市中的实际活动。因此，社交媒体活动中的意外行为是该城市活动意外变化的可靠证据。在本文中，我们介绍了一种混合解决方案，该方法基于对从LBSN收集的数据的两种方法（使用熵分析和聚类技术的组合）的组合，可以及早发现这些变化。特别是，我们已经对在纽约市从Instagram收集的数据集进行了七个月的实验，获得了可喜的结果。