Exploring the Role of Argument Structure in Online Debate Persuasion
This work addresses the challenge of assessing persuasion in online debates for NLP researchers, but it is incremental as it builds on existing methods.
The paper tackled the problem of predicting persuasiveness in online debates by investigating the role of argument structure, finding that incorporating these features improved predictive performance.
Online debate forums provide users a platform to express their opinions on controversial topics while being exposed to opinions from diverse set of viewpoints. Existing work in Natural Language Processing (NLP) has shown that linguistic features extracted from the debate text and features encoding the characteristics of the audience are both critical in persuasion studies. In this paper, we aim to further investigate the role of discourse structure of the arguments from online debates in their persuasiveness. In particular, we use the factor graph model to obtain features for the argument structure of debates from an online debating platform and incorporate these features to an LSTM-based model to predict the debater that makes the most convincing arguments. We find that incorporating argument structure features play an essential role in achieving the better predictive performance in assessing the persuasiveness of the arguments in online debates.