  The limited information (data voids) on political topics relevant to
underrepresented communities has facilitated the spread of disinformation.
Independent journalists who combat disinformation in underrepresented
communities have reported feeling overwhelmed because they lack the tools
necessary to make sense of the information they monitor and address the data
voids. In this paper, we present a system to identify and address political
data voids within underrepresented communities. Armed with an interview study,
indicating that the independent news media has the potential to address them,
we designed an intelligent collaborative system, called Datavoidant.
Datavoidant uses state-of-the-art machine learning models and introduces a
novel design space to provide independent journalists with a collective
understanding of data voids to facilitate generating content to cover the
voids. We performed a user interface evaluation with independent news media
journalists (N=22). These journalists reported that Datavoidant's features
allowed them to more rapidly while easily having a sense of what was taking
place in the information ecosystem to address the data voids. They also
reported feeling more confident about the content they created and the unique
perspectives they had proposed to cover the voids. We conclude by discussing
how Datavoidant enables a new design space wherein individuals can collaborate
to make sense of their information ecosystem and actively devise strategies to
prevent disinformation.
