An Uncertainty-Aware Approach for Exploratory Microblog Retrieval
Mengchen Liu, Shixia Liu, Xizhou Zhu, Qinying Liao, Furu Wei, and, Shimei Pan

TL;DR
This paper presents an uncertainty-aware visual analytics method for retrieving salient microblog posts, users, and hashtags, enhancing data discovery and refinement through multifaceted ranking and interactive visualization.
Contribution
It introduces an extension of ranking techniques with uncertainty modeling and a composite visualization for improved microblog data retrieval and analysis.
Findings
Effective retrieval of salient microblog data demonstrated on Twitter datasets
Uncertainty visualization helps identify and refine uncertain results
Qualitative evaluation and case studies validate approach effectiveness
Abstract
Although there has been a great deal of interest in analyzing customer opinions and breaking news in microblogs, progress has been hampered by the lack of an effective mechanism to discover and retrieve data of interest from microblogs. To address this problem, we have developed an uncertainty-aware visual analytics approach to retrieve salient posts, users, and hashtags. We extend an existing ranking technique to compute a multifaceted retrieval result: the mutual reinforcement rank of a graph node, the uncertainty of each rank, and the propagation of uncertainty among different graph nodes. To illustrate the three facets, we have also designed a composite visualization with three visual components: a graph visualization, an uncertainty glyph, and a flow map. The graph visualization with glyphs, the flow map, and the uncertainty analysis together enable analysts to effectively find the…
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Taxonomy
TopicsData Visualization and Analytics · Complex Network Analysis Techniques · Advanced Text Analysis Techniques
