Finding socio-Textual associations among locations

An increasing amount of user-generated content on the Web is geotagged. This often results in the formation of user trails, e.g., sequences of photos, check-ins, or text messages, that users generate while visiting various locations. In this paper, we introduce and study the problem of identifying sets of locations that are strongly associated under social and textual criteria. We say that a location set is associated with a set of keywords if there exists a user with posts around these locations whose textual descriptions cover all keywords. We measure the strength of this association by the number of users with posts that support it. Although the problem reminisces frequent itemset mining, we show that our support measure does not satisfy the necessary anti-monotonicity property, which is used to effectively prune the search space. Nonetheless, by studying the characteristics of the support measure, we are able to devise an efficient approach. We present a basic and two optimized algorithms, exploiting an inverted or a spatio-textual index to increase efficiency. Finally, we conduct an experimental evaluation using geotagged Flickr photos in three major cities. From a qualitative perspective, the results indicate that the introduced type of query returns meaningful and interesting location sets, which are not discovered by other existing approaches. Furthermore, the proposed optimizations and the use of appropriate indexes significantly reduce computation time.