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The growing popularity of interactive time series exploration platforms has made data visualization more accessible to the public. However, the ease of creating polished charts with preloaded data also enables selective information presentation, often resulting in biased or misleading visualizations. Research shows that these tools have been used to spread misinformation, particularly in areas such as public health and economic policies during the COVID-19 pandemic. Post hoc fact-checking may be ineffective because it typically addresses only a portion of misleading posts and comes too late to curb the spread. In this work, we explore using visualization design to counteract cherry-picking, a common tactic in deceptive visualizations. We propose a design space of guardrails—interventions to expose cherry-picking in time-series explorers. Through three crowd-sourced experiments, we demonstrate that guardrails, particularly those superimposing data, can encourage skepticism, though with some limitations. We provide recommendations for developing more effective visualization guardrails.
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Perturbation-based Detection and Resolution of Cherry-picking
In settings where an outcome, a decision, or a statement is made based on a single option among alternatives, it is popular to cherry-pick the data to generate an outcome that is supported by the cherry-picked data but not in general. In this paper, we use perturbation as a technique to design a support measure to detect, and resolve, cherry-picking across different contexts. In particular, to demonstrate the general scope of our proposal, we study cherry picking in two very different domains: (a) political statements based on trend-lines and (b) linear rankings. We also discuss sampling-based estimation as an effective and efficient approximation approach for detecting and resolving cherry-picking at scale.
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- Award ID(s):
- 2107290
- PAR ID:
- 10300669
- Editor(s):
- Wang, Haixun; Li, Chengkai; Yang, Jun
- Date Published:
- Journal Name:
- A Quarterly bulletin of the Computer Society of the IEEE Technical Committee on Data Engineering
- Volume:
- 45
- Issue:
- 3
- ISSN:
- 1053-1238
- Page Range / eLocation ID:
- 39-51
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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