MithraDetective: A System for Cherry-picked Trendlines Detection
Yoko Nagafuchi, Yin Lin, Kaushal Mamgain, Abolfazl Asudeh, H. V., Jagadish, You (Will) Wu, Cong Yu
TL;DR
MithraDetective is a system designed to evaluate and visualize the support of reported trends in data, helping identify cherry-picked samples and discover better-supported alternatives.
Contribution
It introduces a support score metric and an interactive visual interface for detecting cherry-picked trendlines in data.
Findings
Supports identifying misleading trend claims
Helps discover more representative trendlines
Provides an interactive visual analysis tool
Abstract
Given a data set, misleading conclusions can be drawn from it by cherry-picking selected samples. One important class of conclusions is a trend derived from a data set of values over time. Our goal is to evaluate whether the 'trends' described by the extracted samples are representative of the true situation represented in the data. We demonstrate MithraDetective, a system to compute a support score to indicate how cherry-picked a statement is; that is, whether the reported trend is well-supported by the data. The system can also be used to discover more supported alternatives. MithraDetective provides an interactive visual interface for both tasks.
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Taxonomy
TopicsData Visualization and Analytics · Time Series Analysis and Forecasting · Video Analysis and Summarization