We present a visual computing framework for analyzing moral rhetoric on social media around controversial topics. Using Moral Foundation Theory, we propose a methodology for deconstructing and visualizing the \textit{when}, \textit{where}, and \textit{who} behind each of these moral dimensions as expressed in microblog data. We characterize the design of this framework, developed in collaboration with experts from language processing, communications, and causal inference. Our approach integrates microblog data with multiple sources of geospatial and temporal data, and leverages unsupervised machine learning (generalized additive models) to support collaborative hypothesis discovery and testing. We implement this approach in a system named MOTIV. We illustrate this approach on two problems, one related to Stay-at-home policies during the COVID-19 pandemic, and the other related to the Black Lives Matter movement. Through detailed case studies and discussions with collaborators, we identify several insights discovered regarding the different drivers of moral sentiment in social media. Our results indicate that this visual approach supports rapid, collaborative hypothesis testing, and can help give insights into the underlying moral values behind controversial political issues. Supplemental Material: https://osf.io/ygkzn/?view_only=6310c0886938415391d977b8aae8b749

翻译：我们提出一个视觉计算框架，用于分析社交媒体上围绕争议话题的道德修辞。基于道德基础理论，我们提出了一种解构与可视化微博数据中各道德维度“何时”、“何地”及“何人”表达的方法论。我们描述了与语言处理、传播学和因果推断领域专家合作开发的该框架设计。该方法整合微博数据与多源地理空间及时间序列数据，并利用无监督机器学习（广义加性模型）支持协作性假设发现与检验。我们将该方法实现为一个名为MOTIV的系统。通过两个案例进行阐述：其一涉及新冠疫情期间的居家政策，其二关联“黑人的命也是命”运动。通过与合作者的详细案例研究与讨论，我们识别出关于社交媒体道德情绪不同驱动因素的若干洞见。结果表明，该视觉方法支持快速协作式假设检验，并能帮助洞察争议政治议题背后的潜在道德价值观。补充材料：https://osf.io/ygkzn/?view_only=6310c0886938415391d977b8aae8b749