Discovering and Ranking Urban Social Clusters Out of Streaming Social Media Datasets

Abstract

Urban social media mining is the process of discovering urban patterns from spatio-temporal social media datasets. Urban social clusters are the clusters formed by the social media posts of users living in cities at a certain time and place. Discovering and identifying urban social clusters is of great importance for urban and regional planning, target audience identification, a better understanding of city dynamics and so forth. Discovering and ranking urban social clusters out of streaming social media datasets require efficient filtering approaches and mining algorithms. In the literature, there are several studies performed that address the discovery of the importance of urban clusters. Most of these studies take into account the spatial expansions over time and the changes in the numbers of elements within clusters when identifying the significance of urban clusters. However, in contrast to these studies, we have also considered cluster temporal formation stability, spatial density variation, and the impact of meta-information on urban social clusters. In this study, Temporal, Spatial, and Meta Important Urban Social Clusters Miner (TSMIUSC-Miner) algorithm is proposed. In the proposed algorithm, urban social clusters are discovered, and their importance relative to each other are compared and ranked. The temporal, spatial and meta importance scores of the clusters are calculated and then, the clusters that satisfy predefined score thresholds are discovered. The performance of the proposed TSMIUSC-Miner algorithm compared with that of a naive approach using real-life streaming Twitter/X dataset. The results showed that the proposed TSMIUSC-Miner algorithm outperforms the naive approach in terms of execution time.