Political polarisation on structured discussion platforms such as Reddit differs fundamentally from that on broadcast platforms such as Twitter/X, yet most prior work targets the latter. We present an end-to-end framework for measuring and analysing polarisation dynamics, applied to the r/Brexit subreddit (871K submissions, November 2015 -- February 2021). We construct r/Brexit, a crowd-annotated stance dataset of 5,895 labelled submissions (inter-annotator agreement = 0.804), and train a domain-adapted BERT classifier. We introduce a continuous polarity metric that replaces discrete stance categories, revealing fine-grained opinion spectra across 27 politically-defined periods. Our analysis yields three key findings: (a) future stance prediction is confounded by survivorship bias: persuadable users disengage, and those who remain are already entrenched; (b) echo chambers are quantifiably dominant, with nearly 40% of interactions between like-minded users; (c) user current polarity is the dominant predictor of future polarity, with echo-chamber immersion as the secondary predictive signal. These findings reveal that Reddit's partisan core is entrenched by self-selection, not softened by cross-cutting exposure.

翻译：在Reddit这类结构化讨论平台上的政治极化现象与Twitter/X等广播平台存在本质差异，然而既有研究多聚焦于后者。本文提出一个端到端的极化动态测量与分析框架，并将其应用于r/Brexit子论坛（87.1万条发帖，2015年11月至2021年2月）。我们构建了包含5,895条标注发帖的众包立场数据集r/Brexit（标注者间一致性=0.804），并训练了一个领域自适应BERT分类器。通过引入替代离散立场分类的连续极性度量，我们揭示了跨越27个政治定义时期的细粒度意见光谱。分析得出三个关键结论：（a）未来立场预测受幸存者偏差干扰：可被说服的用户逐渐退出讨论，留存用户观点早已固化；（b）回音室效应具有量化主导性，近40%的互动发生在观点相似用户之间；（c）用户当前极性是预测未来极性的主导因素，回音室沉浸度构成次要预测信号。这些发现表明，Reddit的党派核心通过自我选择机制固化，而非因跨观点接触而软化。