Narratives in Crowdsourced Evaluation of Visualizations: A Double-Edged Sword?

論文URL:http://dl.acm.org/citation.cfm?doid=3025453.3025870

論文アブストラクト:We explore the effects of providing task context when evaluating visualization tools using crowdsourcing. We gave crowdsource workers i) abstract information visualization tasks without any context, ii) tasks where we added semantics to the dataset, and iii) tasks with two types of backstory narratives: an analytic narrative and a decision-making narrative. Contrary to our expectations, we did not find evidence that adding data semantics increases accuracy, and further found that our backstory narratives can even decrease accuracy. Adding dataset semantics can however increase attention and provide subjective benefits in terms of confidence, perceived easiness, task enjoyability and perceived usefulness of the visualization. Nevertheless, our backstory narratives did not appear to provide additional subjective benefits. These preliminary findings suggest that narratives may have complex and unanticipated effects, calling for more studies in this area.

日本語のまとめ:

視覚化したデータをクラウドソーシングで評価する際、ユーザのデータの意味(背景)の理解度が評価に与える影響を調査した。その結果、評価の正確さは低下させるが評価する際の注意力の高まりや認識の容易さには繋がることがわかった。

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