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Although there has been a great deal of interest in analyzing customeropinions and breaking news in microblogs, progress has been hampered by thelack of an effective mechanism to discover and retrieve data of interest frommicroblogs. To address this problem, we have developed an uncertainty-awarevisual analytics approach to retrieve salient posts, users, and hashtags. Weextend an existing ranking technique to compute a multifaceted retrievalresult: the mutual reinforcement rank of a graph node, the uncertainty of eachrank, and the propagation of uncertainty among different graph nodes. Toillustrate the three facets, we have also designed a composite visualizationwith three visual components: a graph visualization, an uncertainty glyph, anda flow map. The graph visualization with glyphs, the flow map, and theuncertainty analysis together enable analysts to effectively find the mostuncertain results and interactively refine them. We have applied our approachto several Twitter datasets. Qualitative evaluation and two real-world casestudies demonstrate the promise of our approach for retrieving high-qualitymicroblog data.

An Uncertainty-Aware Approach for Exploratory Microblog Retrieval