Mitigating Linguistic Aggression in Group Decision-Making: A Comparative Analysis of AI-Driven Hostility Detection

Abstract

The process of group decision-making is an integral component not only for quotidian interactions but also for strategic deliberations. However, it is profoundly shaped by the inherent semantic indeterminacy of natural language. This linguistic ambiguity starkly contrasts the syntactic and semantic precision characteristic of machine-generated language. Furthermore, the conveyance of affective states–such as aggressiveness or elation–via natural language introduces a layer of complexity that can significantly perturb the equilibrium of the group decision-making process. In response to these challenges, we propose an advanced consensus-reaching methodology based on sentiment analysis to quantify and mitigate aggressiveness in discourse. This study conducts a comparative evaluation of three state-of-the-art large language models: Gemini, Copilot, and ChatGPT for their efficacy in detecting and assessing hostility. By calibrating the influence of individual participants based on their degree of linguistic aggression, the proposed framework attenuates the disproportionate impact of dominant voices, thus fostering a more balanced and equitable deliberative environment. This methodological innovation not only incentivizes the adoption of a more dispassionate and constructive linguistic register but also safeguards the integrity of collective decision-making processes against the distortive effects of undue emotional influence. Across five repeated evaluations per comment, ChatGPT and Gemini exhibited < 5% variance, while Copilot showed ≈ 8 − 12%; in all cases, hostility-aware weighting reduced the most aggressive expert’s influence by ≈ 27 − 29%, yielding robust group rankings. These mechanisms improve consensus quality by reducing bias from aggressive discourse, and they are expected to foster higher group satisfaction through perceived fairness in deliberation. Potential improvements include benchmarking against gold standards, extending to multilingual and multimodal contexts, and enhancing transparency for end-users.