Relevance-driven clustering for visual information retrieval on twitter

Abstract

Geo-temporal visualization of Twitter search results is a challenging task since the simultaneous display of all matching tweets would result in a saturated display. In such settings, clustering search results can assist users to scan only a few coherent groups of related tweets rather than many individual tweets. However, in practice, the use of unsupervised clustering methods such as k-means does not necessarily guarantee that the clusters themselves are relevant. Therefore, we develop a novel method of relevance-driven clustering for visual information retrieval to supply users with highly relevant clusters representing different information perspectives of their queries. We specifically propose a Visual Twitter Information Retrieval (Viz-TIR) tool which based on a fast greedy algorithm that optimizes an approximation of an expected F1-Score metric to generate these clusters. We demonstrate its effectiveness w.r.t. k-means and a baseline method that shows all top matching results on a scenario related to searching natural disasters in US-based Twitter data. Our demo shows that Viz-TIR is easy to use and more precise in extracting geo-temporally coherent clusters given search queries in comparison to k-means, thus aiding the user in visually searching and browsing social network content. Overall, we believe this work enables new opportunities for the synthesis of information retrieval as well as combined relevance and display-aware optimization techniques to support query-adaptive visual information exploration interfaces.