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

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.

• The paper introduces a self-attentive neural network model that identifies linguistic triggers and contextual cues for ad hominem fallacies in online discussions.
• It demonstrates that controversial initial posts significantly increase the likelihood of direct ad hominem responses in digital debates.
• The study provides empirical evidence to inform automated moderation, offering actionable insights for improving online discourse quality.

Analyzing the Dynamics of Ad Hominem Arguments in Web Discussions

The paper "Before Name-calling: Dynamics and Triggers of Ad Hominem Fallacies in Web Argumentation" by Habernal et al. presents an empirical investigation into the occurrence, characteristics, and triggers of ad hominem arguments in web-based discussions, specifically focusing on the Reddit platform "Change My View" (CMV). This study addresses a notable gap in existing literature by examining both the theoretical and practical aspects of ad hominem fallacies within dynamic dialogical exchanges.

Research Objectives and Approach

The authors set out to explore three primary research questions: the nature of ad hominem arguments in web debates, the extent of contextual information required for recognizing ad hominem arguments, and the triggers that lead discussions into ad hominem exchanges. The complexity of discourse is examined at three distinct levels: isolated ad hominem arguments, direct ad hominem interactions with original posts, and within wider interpersonal discourse contexts.

The research utilizes large-scale data collection and annotation from CMV discussions, involving the creation of new datasets and experiments employing various neural network architectures to derive insights. A noteworthy methodological contribution is the development of a self-attentive neural network model capable of identifying linguistic and rhetorical triggers of ad hominem arguments.

Key Insights and Empirical Findings

Several significant findings emerge from this study:

1. Typology and Recognition of Ad Hominem Arguments: The paper challenges existing theoretical taxonomies of ad hominem arguments by identifying multifaceted linguistic triggers. Through empirical data analysis and annotation studies, various rhetorical devices, such as vulgar insults and loaded language, were found to most frequently characterize ad hominem arguments in online discussions.
2. Role of Post Controversy: The researchers explore the triggers for direct ad hominem responses by analyzing the controversy level of original posts. It is shown that ad hominem attacks are more frequent in threads with highly controversial initial posts, while the perceived reasonableness of an argument is less predictive of ad hominem engagement.
3. Contextual Dynamics: In exploring ad hominem exchanges within dialogue, the paper identifies attention-capturing words or phrases, such as sarcasm, directive language, and accusations of fallacies, as potential triggers. These findings are derived from the heat map analyses of neural network attention mechanisms, offering a novel way to interpret the dynamics of argumentative fallacies.

The study's quantitative results, such as the 0.810 accuracy in ad hominem classification, underscore the feasibility of automatically flagging such arguments, supporting potential moderation tools in online discussion platforms.

Implications and Future Directions

From a theoretical perspective, this research contributes to both argumentation theory and cognitive linguistics by offering empirical data on the prevalence and triggers of fallacious reasoning in digital communication. Practically, the insights on language use and interaction dynamics can inform the design of systems aimed at fostering civil discourse and automated moderation tools to mitigate uncivil behavior online.

The authors also highlight areas for further inquiry, including the role of participant metadata, personality traits, and the need for a comprehensive post-verification of the proposed typology. The interaction between participant behavior patterns and fallacy occurrence is a promising area for future work, particularly in the context of developing algorithms with greater specificity in flagging non-constructive debates.

Overall, this paper provides an extensive empirical analysis of ad hominem fallacies within web argumentation, offering valuable insights for improving the quality of online discussions and advancing the understanding of negative argumentative behaviors in digital spaces.