An Uncertainty-Aware Approach for Exploratory Microblog Retrieval

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 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.