  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.
