Before Name-calling: Dynamics and Triggers of Ad Hominem Fallacies in Web Argumentation

TL;DR

This paper investigates the dynamics and triggers of ad hominem fallacies in online debates through large-scale annotations, neural experiments, and linguistic analysis, aiming to understand their causes and improve debate quality.

Contribution

It provides the first large-scale empirical analysis of ad hominem fallacies, explores neural models for detecting triggers, and offers linguistic insights into their causes.

Findings

Neural models can identify ad hominem triggers with high accuracy.

Controversy and perceived reasonableness are key factors in ad hominem occurrences.

Linguistic cues such as personal attacks and emotional language are significant triggers.

Abstract

Arguing without committing a fallacy is one of the main requirements of an ideal debate. But even when debating rules are strictly enforced and fallacious arguments punished, arguers often lapse into attacking the opponent by an ad hominem argument. As existing research lacks solid empirical investigation of the typology of ad hominem arguments as well as their potential causes, this paper fills this gap by (1) performing several large-scale annotation studies, (2) experimenting with various neural architectures and validating our working hypotheses, such as controversy or reasonableness, and (3) providing linguistic insights into triggers of ad hominem using explainable neural network architectures.