  As public discourse continues to move and grow online, conversations about
divisive topics on social media platforms have also increased. These divisive
topics prompt both contentious and non-contentious conversations. Although what
distinguishes these conversations, often framed as what makes these
conversations contentious, is known in broad strokes, much less is known about
the linguistic signature of these conversations. Prior work has shown that
contentious content and structure can be a predictor for this task, however,
most of them have been focused on conversation in general, very specific
events, or complex structural analysis. Additionally, many models used in prior
work have lacked interpret-ability, a key factor in online moderation. Our work
fills these gaps by focusing on conversations from highly divisive topics
(abortion, climate change, and gun control), operationalizing a set of novel
linguistic and conversational characteristics and user factors, and
incorporating them to build interpretable models. We demonstrate that such
characteristics can largely improve the performance of prediction on this task,
and also enable nuanced interpretability. Our case studies on these three
contentious topics suggest that certain generic linguistic characteristics are
highly correlated with contentiousness in conversations while others
demonstrate significant contextual influences on specific divisive topics.
