评估情感目标：数据可视化中的统计麻木
Evaluating Affective Objectives: Statistical Numbing in Data Visualization
AI总结：
探讨通过数据驱动、人类驱动或混合叙事三种设计策略传达人道主义危机，以数据视频形式研究统计麻木影响，发现人类驱动叙事获最高捐款，混合叙事最低，还讨论了研究局限及影响。
摘要：
可视化可助观众理解灾难规模，设计师常关注传达定量信息，但这可能无法反映更高层次情感目标，甚至产生统计麻木。本文探索三种传达人道主义危机的设计策略，通过实证研究发现人类驱动叙事引发最高捐款，混合叙事最低，并讨论了研究局限及数据可视化设计中情感目标与同理心麻木的影响。
英文摘要：
Visualizations can help audiences understand the scale of tragedies, such as the consequences of natural disasters, war, genocide, and pandemics. In these cases, a visualization designer's default behavior may be to focus on communicating quantitative information: numbers, statistics, and trends. However, this may not reflect higher-level affective objectives to inspire their audience to care about an issue, empathize with others, or take action to help those in need. Worse, standard visualizations may conflict with these goals, as statistics can numb emotions and reduce prosocial feelings toward people in need. Designers have developed strategies to increase affective responses through data visualizations, such as blending data narratives and personal narratives about individuals. In this paper, we explore three design strategies for communicating a humanitarian crisis: data-driven, human-driven, or mixed narratives. We conducted an empirical study to explore the effect of statistical numbing in the context of these types of narratives in the format of data videos. In particular, we measure prosocial feelings and behaviors by giving participants the option of donating money as part of the study. We find that human-driven narratives (photographs and stories of individuals) elicited the highest donations and that the mixed narrative combination led to the lowest donations. We discuss the limitations of this study and the implications of pursuing affective objectives and the numbing of empathy in data visualization design.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
