Exploring the Role of Argument Structure in Online Debate Persuasion (2020)

Abstract: 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 best predictive performance in assessing the persuasiveness of the arguments on online debates.

## 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 @xcite @xcite , characteristics of audience (i.e. prior beliefs, demographics) @xcite and social interactions @xcite . prior work has showed incorporating argument structure features is important in assessing the quality of monological persuasive essays @xcite . @xcite and @xcite further collected annotations for semantic types of argument components and studied the relationship between the semantic types and persuasiveness of the arguments from online argumentative platform change-myview (cmv) @xcite . 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 moderators @xcite , 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 @xcite where debaters from two diverging sides of an issue express their opinions on a controversial topic in turns since these debates are more structured and the persuasiveness of the arguments in debates are evaluated by a larger set of audience. moreover, this setup allows us to account for the audience characteristics when studying the effect of the argument structure on persuasion. we first generate argument structure on ddo dataset @xcite ) using the model proposed by @xcite . we then incorporate the features extracted from argument structure to an lstm-based model that encodes the sequence of turns from two sides (i.e. pro vs. con). we compare our results with the baselines proposed in @xcite which extracts linguistics features from the debate text as well as features that encode characteristics of the audience. we find that incorporating argument structure features achieves significantly better results than the baselines. our analysis further shows that argument structure features encode important strategies of persuasion, for example, we find that more convincing arguments are more likely to in-figure 1 : an example of argument structure extracted from the debate text in one round from one side. ## dataset we experiment with ddo dataset @xcite which includes 77,655 debates covering 23 different topic categories. each debate consists of multiple rounds with each round containing one utterance from the pro side and one utterance from the con side. besides the text information for debates, the dataset also contains user information and votes provided by the audience on six different criteria of evaluating both the debaters. we use the criterion "made more convincing arguments" as an overall signal to study the role of argument structure in predicting more convincing arguments. ## experiments and analysis we compare our model with the baseline proposed by @xcite employing linguistic features and features encoding audience characteristics. the prediction accuracy is evaluated using 5-fold cross-validation, and the model parameters for each split are picked with 3-fold crossvalidation on the training set. as shown in we also look into some examples that are classified wrong by the model. a typical error is caused by wrong proposition type classification. for example, in the debate "driving on public roads is a right not a privilege", sentences from pro side "in addition, in purchasing our vehicles, we have the right to drive said vehicle." and "i appreciate the insight given by my opponent but he/she has failed to address the issue at hand." are classified as "testimony" wrongly, which makes the model prefer pro as the more convincing side. we believe that incorporating more accurate argument structure generation models can further improve the performance on persuasion prediction. ## conclusion in this work, we explore the role of argument structure in online debate persuasion and find that incorporating argument structure features along with the linguistic features achieves the best predictive performance models. moreover, we observe that argument structure features provide important cues about effective persuasion strategies in online debates.