Spatiotemporal topic association detection on tweets

The analysis of Twitter data can help to predict or explain many real world phenomena. The relationships among events in the real world can be reflected among the topics on social media. In this paper, we propose the concept of topic association and the associated mining algorithms. Topics with close temporal and spatial relationship may have direct or potential association in the real world. Our goal is to mine such topic associations and show their relationships in different time-region frames. We propose to use the concepts of participation ratio and participation index to measure the closeness among topics and propose a spatiotemporal index to calculate them efficiently. With the topic filtering and the topic combination, we further optimize the mining process and the mining results. The algorithms are evaluated on a Twitter dataset with 27,956,257 tweets.