Using Argumentative Structure to Interpret Debates in Online Deliberative Democracy and eRulemaking

John Lawrence, Joonsuk Park, Katarzyna Budzynska, Claire Cardie, Barbara Konat, Chris Reed

Abstract:

Governments around the world are increasingly utilising online platforms and social media to engage with,
and ascertain the opinions of, their citizens. Whilst policy makers could potentially benefit from such enor-
mous feedback from society, they first face the challenge of making sense out of the large volumes of data
produced. In this article, we show how the analysis of argumentative and dialogical structures allows for
the principled identification of those issues that are central, controversial, or popular in an online corpus
of debates. Although areas such as controversy mining work towards identifying issues that are a source of
disagreement, by looking at the deeper argumentative structure, we show that a much richer understanding
can be obtained. We provide results from using a pipeline of argument-mining techniques on the debate
corpus, showing that the accuracy obtained is sufficient to automatically identify those issues that are key to
the discussion, attracting proportionately more support than others, and those that are divisive, attracting
proportionately more conflicting viewpoints.