PROJECT TITLE :

An Uncertainty-Aware Approach for Exploratory Microblog Retrieval

ABSTRACT:

Although there was a nice deal of interest in analyzing client opinions and breaking news in microblogs, progress has been hampered by the dearth of an efficient mechanism to discover and retrieve data of interest from microblogs. To handle this problem, we have a tendency to 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 every rank, and therefore the propagation of uncertainty among different graph nodes. To illustrate the three sides, we have a tendency to have additionally designed a composite visualization with three visual elements: a graph visualization, an uncertainty glyph, and a flow map. The graph visualization with glyphs, the flow map, and also the uncertainty analysis along enable analysts to effectively notice the foremost unsure results and interactively refine them. We have a tendency to have applied our approach to many Twitter datasets. Qualitative evaluation and 2 real-world case studies demonstrate the promise of our approach for retrieving high-quality microblog data.