Exploring the Role of Argument Structure in Online Debate Persuasion

Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, pages 8905–8912, November 16–20, 2020. c⃝2020 Association for Computational Linguistics 8905 Exploring the Role of Argument Structure in Online Debate Persuasion Jialu Li Cornell University jl3855@cornell.edu Esin Durmus Cornell University ed459@cornell.edu Claire Cardie Cornell University cardie@cs.cornell.edu Abstract Online debate forums provide users a platform to express their opinions on controversial top- ics while being exposed to opinions from di- verse set of viewpoints. Existing work in Nat- ural Language Processing (NLP) has shown that linguistic features extracted from the de- bate text and features encoding the character- istics of the audience are both critical in per- suasion studies. In this paper, we aim to fur- ther investigate the role of discourse structure of the arguments from online debates in their persuasiveness. In particular, we use the fac- tor graph model to obtain features for the argu- ment structure of debates from an online debat- ing 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 de- bates. 1 Introduction The increase in availability of online argumentation platforms has provided opportunity for researchers to develop computational methods at a larger scale studying the important factors of persuasiveness such as the language use (Hidey et al., 2017; Tan et al., 2016; Zhang et al., 2016), characteristics of audience (i.e. prior beliefs, demographics) (Dur- mus and Cardie, 2019a, 2018) and social interac- tions (Durmus and Cardie, 2019b). Prior work has showed incorporating argument structure features is important in assessing the qual- ity of monological persuasive essays (Klebanov et al., 2016; Wachsmuth et al., 2016). Hidey et al. (2017) and Egawa et al. (2019) further collected annotations for semantic types of argument com- ponents and studied the relationship between the semantic types and persuasiveness of the argu- ments from online argumentative platform Change- MyView (CMV) (Tan et al., 2016). CMV consists of discussion trees where the users interact with the original poster to change their opinion on a given topic. Although the discussion trees are of a high quality since they are monitored by moder- ators (Tan et al., 2016), they are not as structured since any user in the subreddit can participate in the discussions once the original post is posted. Furthermore, the persuasiveness of the posts in CMV is evaluated only by the original poster (i.e. whether they change their stance or not). In this paper, we aim to investigate the effect of argument structure in persuasion on online debates. We focus on debates from DDO corpus (Durmus and Cardie, 2019a) where debaters from two diverging sides of an issue express their opinions on a controversial topic in turns since these debates are more struc- tured and the persuasiveness of the arguments in debates are evaluated by a larger set of audience. Moreover, this setup …

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