An Uncertainty-Aware Approach for Exploratory Microblog Retrieval
By Mengchen Liu, Shixia Liu, Xizhou Zhu, Qinying Liao, Furu Wei, Shimei Pan. 2015
63 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Statistical and Nonlinear Physics
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Citations in the corpus
- Towards Better Analysis of Deep Convolutional Neural Networks (2016, background): “Thus, we decided to allow users to switch between these facets [38] instead of overlaying multiple facets (R3).”
- Towards better analysis of machine learning models: A visual analytics perspective (2017, methodology): “From the information visualization community, researchers have designed a number of techniques for user initiative guidance [14, 30, 33, 34, 49], where the user is the active participant in improving and refining the performance and learning results.”
- A Multidisciplinary Survey and Framework for Design and Evaluation of Explainable AI Systems (2018, methodology): “tions in XAI studies. Satisfaction Measures Evaluation Methods User Satisfaction Interview and Self-report ([25, 62, 124, 125]) Likert-scale Questionnaire ([39, 62, 112, 124, 125]) Expert Case Study ([95, 106, 128, 130, 193]) Explanation Usefulness Engagement with Explanations ([39]) Task Duration a”