Spatiotemporal Topic Association Detection on Tweets

Published: 01 Nov 2016, Last Modified: 15 May 2025Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (SIGSPATIAL ’16)EveryoneCC BY-ND 4.0
Abstract: We introduce the concept of topic association—how topics co-occur in both space and time—and present mining algorithms to detect these associations on Twitter. Our framework identifies topic entities, builds spatiotemporal co-occurrence matrices, and applies pattern mining to find significant associations. Real-world Twitter datasets demonstrate the method’s effectiveness and efficiency.
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