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 5,024 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 findings: (a) future stance prediction is confounded by survivorship bias: who remains active is self-selected on engagement, not stance, biasing any longitudinal model toward a non-representative minority; (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：在像Reddit这样的结构化讨论平台上，政治极化从根本上不同于广播型平台（如Twitter/X），然而大多数先前研究都聚焦于后者。我们提出一个端到端框架用于测量和分析极化动态，并将其应用于r/Brexit子版块（871K条帖子，2015年11月至2021年2月）。我们构建了r/Brexit——一个包含5,024条人工标注立场的数据集（注释者间一致性=0.804），并训练了一个领域自适应的BERT分类器。我们引入了一个连续极性度量方法，用以取代离散的立场类别，从而揭示出跨越27个政治定义时期的细致意见光谱。我们的分析得出三项发现：（a）未来立场预测受到幸存者偏差的干扰：谁保持活跃是由参与度自我选择的，而非立场，这导致任何纵向模型都偏向于一个非代表性的少数群体；（b）回音室量化地占据主导地位，近40%的互动发生在观点相似的用户之间；（c）用户当前极性是未来极性的主要预测因子，而回音室沉浸度是次要预测信号。这些发现表明，Reddit的党派核心是由自我选择固化的，而非通过跨领域接触而软化。