title: An Uncertainty-Aware Approach for Exploratory Microblog Retrieval.
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 most uncertain results and interactively refine them. We have applied our approach to several Twitter datasets. Qualitative evaluation and two real-world case studies demonstrate the promise of our approach for retrieving high-quality microblog data.
authors.forename: Mengchen
authors.initials: M
authors.lastname: Liu
authors.forename: Shixia
authors.initials: S
authors.lastname: Liu
authors.forename: Xizhou
authors.initials: X
authors.lastname: Zhu
authors.forename: Qinying
authors.initials: Q
authors.lastname: Liao
authors.forename: Furu
authors.initials: F
authors.lastname: Wei
authors.forename: Shimei
authors.initials: S
authors.lastname: Pan
mesh_terms: D057218:Blogging / Q000145:classification*; D003196:Computer Graphics; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods*; D020407:Internet; D015233:Models, Statistical; D008962:Models, Theoretical; D009010:Monte Carlo Method