Online debate platforms offer a unique window into the mechanisms driving opinion formation: they capture both explicit political preferences and the peer environment in which those preferences are expressed. In this work, I develop a Bayesian logistic regression model, inspired by ideal point models from political science, to disentangle two competing mechanisms of voting behaviour in online debates: conviction, driven by prior ideological beliefs, and conformity, driven by peer influence. I apply this framework to the Debate.org dataset, comprising approximately 341k votes across 78k debates on 48 socio-political topics. As the debate platform does not provide predefined topic labels for each debate, I infer the topic and stance from the debate text using large language models, and, with a Bayesian approach, I quantify the relative contribution of each mechanism. I find substantial heterogeneity across topics: conviction dominates on issues tied to personal freedoms and lifestyle choices, such as drug legalisation and legalised prostitution, while conformity dominates on several topics widely regarded as paradigmatic cases of moral conviction, including abortion, gun rights, and global warming. These results have implications for the stability of online political discourse and the design of deliberative platforms.

翻译：在线辩论平台为了解意见形成的机制提供了独特窗口：它们既捕捉了明确的政治偏好，也记录了这些偏好得以表达的同伴环境。本研究受政治科学中的理想点模型启发，构建了一个贝叶斯逻辑回归模型，以解构在线辩论中投票行为的两种竞争机制：由先前意识形态信念驱动的“信念”机制，以及由同伴影响驱动的“从众”机制。我将该框架应用于 Debate.org 数据集，该数据集涵盖 78,000 场关于 48 个社会政治主题的辩论中约 341,000 次投票。由于辩论平台未为每场辩论提供预定义的主题标签，我利用大语言模型从辩论文本中推断主题与立场，并采用贝叶斯方法量化每种机制的相对贡献。研究发现不同主题间存在显著的异质性：在涉及个人自由和生活方式选择（如毒品合法化和卖淫合法化）的问题上，信念机制占主导地位；而在堕胎、枪支权利和全球变暖等多个通常被视为道德信念典型案例的主题上，从众机制则占据主导。这些结果对在线政治话语的稳定性以及协商平台的设计具有启示意义。