  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
