Deciphering Personal Argument Styles : A Comprehensive Approach to Analyzing Linguistic Properties of Argument Preferences

Abstract

In this paper, we introduce an application for exploring the effect of linguistic features on personalized argument preferences. These individual preferences are derived by measuring the impact of linguistic features on pairwise comparisons between arguments. The insights derived from this are, in turn, useful for studies of argument quality. To conduct this research, we have developed a new pipeline that covers three major components: data collection, argument comparison labeling, and data exploration, incorporating linguistic annotations of arguments and preference data. The first component has resulted in a novel corpus consisting of minimal pairs of arguments: the comparable argument corpus. For the second component, we have developed a visual interactive labeling system that structures the annotation process of pairwise comparisons. Through these annotations, we extract patterns of argument preferences using Gaussian Process Preference Learning based on linguistic feature vectors. The corresponding, personalized models are used to identify relevant features to explain argument preferences. By training individual models for different users, we gain information that allows us to compare different user groups, identifying different argumentation preferences across groups. Each of these steps is supported by novel visual analytics dashboards, facilitating data collection and annotation steps and enabling the exploration of personal preferences.