What you say or how you say it? Predicting Conflict Outcomes in Real and LLM-Generated Conversations

Priya Ronald D'Costa, Evan Rowbotham, Xinlan Emily Hu
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Abstract

When conflicts escalate, is it due to what is said or how it is said? In the conflict literature, two theoretical approaches take opposing views: one focuses on the content of the disagreement, while the other focuses on how it is expressed. This paper aims to integrate these two perspectives through a computational analysis of 191 communication features -- 128 related to expression and 63 to content. We analyze 1,200 GPT-4 simulated conversations and 12,630 real-world discussions from Reddit. We find that expression features more reliably predict destructive conflict outcomes across both settings, although the most important features differ. In the Reddit data, conversational dynamics such as turn-taking and conversational equality are highly predictive, but they are not predictive in simulated conversations. These results may suggest a possible limitation in simulating social interactions with language models, and we discuss the implications for our findings on building social computing systems.
说什么还是怎么说?预测真实对话和 LLM 生成的对话中的冲突结果
当冲突升级时,是因为说了什么还是怎么说的?在冲突文献中,有两种理论观点截然相反:一种侧重于分歧的内容,而另一种则侧重于分歧的表达方式。本文旨在通过对 191 个交流特征(其中 128 个与表达有关,63 个与内容有关)进行计算分析,将这两种观点进行整合。我们分析了 1,200 个 GPT-4 模拟对话和 12,630 个来自 Reddit 的真实讨论。我们发现,在两种情况下,表达特征都能更可靠地预测破坏性冲突的结果,尽管最重要的特征有所不同。在 Reddit 数据中,诸如轮流发言和会话平等等会话动态具有很高的预测性,但在模拟会话中却不具有预测性。这些结果表明,用语言模型模拟社交互动可能存在局限性,我们将讨论我们的发现对构建社交计算系统的影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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