  In our world with full of uncertainty, debates and argumentation contribute
to the progress of science and society. Despite of the increasing attention to
characterize human arguments, most progress made so far focus on the debate
outcome, largely ignoring the dynamic patterns in argumentation processes. This
paper presents a study that automatically analyzes the key factors in argument
persuasiveness, beyond simply predicting who will persuade whom. Specifically,
we propose a novel neural model that is able to dynamically track the changes
of latent topics and discourse in argumentative conversations, allowing the
investigation of their roles in influencing the outcomes of persuasion.
Extensive experiments have been conducted on argumentative conversations on
both social media and supreme court. The results show that our model
outperforms state-of-the-art models in identifying persuasive arguments via
explicitly exploring dynamic factors of topic and discourse. We further analyze
the effects of topics and discourse on persuasiveness, and find that they are
both useful - topics provide concrete evidence while superior discourse styles
may bias participants, especially in social media arguments. In addition, we
draw some findings from our empirical results, which will help people better
engage in future persuasive conversations.
