The Impact of Persona-based Political Perspectives on Hateful Content Detection

TL;DR

This study examines whether persona-based prompting in multimodal hate speech detection can replace computationally intensive political pretraining, finding limited correlation between political personas and classification outcomes.

Contribution

It demonstrates that persona-based prompting can achieve comparable fairness in hate speech detection without extensive political pretraining, challenging assumptions about political bias impact.

Findings

Political positioning has little correlation with classification decisions.

Explicit ideological descriptors do not significantly alter model bias.

Persona prompting may suffice for fair hate speech detection without political pretraining.

Abstract

While pretraining language models with politically diverse content has been shown to improve downstream task fairness, such approaches require significant computational resources often inaccessible to many researchers and organizations. Recent work has established that persona-based prompting can introduce political diversity in model outputs without additional training. However, it remains unclear whether such prompting strategies can achieve results comparable to political pretraining for downstream tasks. We investigate this question using persona-based prompting strategies in multimodal hate-speech detection tasks, specifically focusing on hate speech in memes. Our analysis reveals that when mapping personas onto a political compass and measuring persona agreement, inherent political positioning has surprisingly little correlation with classification decisions. Notably, this lack of correlation persists even when personas are explicitly injected with stronger ideological descriptors. Our findings suggest that while LLMs can exhibit political biases in their responses to direct political questions, these biases may have less impact on practical classification tasks than previously assumed. This raises important questions about the necessity of computationally expensive political pretraining for achieving fair performance in downstream tasks.