AI 中文总结
本文基于Reddit数据发现2022年末出现政治话语多元化趋势的不对称断裂，保守派用户话语多元化轨迹中断，进步派无类似变化，机制更可能是社区层面的话语趋同。
AI 中文摘要
本文利用2019-2025年两个跨党派论坛的600万条Reddit评论，记录了政治话语原有多元化趋势中出现的意识形态不对称断裂，该断裂约在2022年末显现。保守派用户经历了此前多元化轨迹的中断，而进步派用户未出现类似变化。这种不对称性在多种估计策略（中断时间序列ITS、双重差分DiD、回归离散设计RDiT、倾向得分匹配）和时间聚合方式中均保持一致。对2377个候选截止日期进行的每日排列检验显示，ChatGPT阈值产生的估计值处于不显著的第49.8百分位，表明该转变是逐渐形成的，而非在单一日期发生断裂。跟踪7次模型发布中AI暴露情况的连续累积LLM指数，在消除二元估计的二次趋势设定下仍保持显著。留存用户分析缩小了机制范围：当样本限制为研究期间始终活跃的作者时，同质化效应消失，且留存用户的置信区间排除了作者内部效应（其规模甚至不足全样本估计值的十分之一）。该机制最简约的解释是生态层面（社区层面的话语趋同），而非个体层面的AI采用，尽管数据无法将该解释与同期长期变化明确区分。
英文摘要
I document an ideologically asymmetric break in the pre-existing diversification trend of political discourse, emerging around late 2022, using 6 million Reddit comments from two cross-partisan forums, 2019-2025. Conservative users experienced an interruption of their prior diversification trajectory; progressive users showed no comparable change. The asymmetry is consistent across estimation strategies (ITS, DiD, RDiT, propensity-score matching) and temporal aggregations. A daily-frequency permutation test over 2,377 candidate cutoff dates shows the ChatGPT threshold produces an unremarkable estimate (49.8th percentile): the shift builds gradually instead of breaking at a single date. A continuous cumulative LLM index, tracking AI exposure across seven model releases, remains significant under a quadratic trend specification that eliminates the binary estimate. A stayer analysis narrows the mechanism: the homogenization effect disappears when the sample is restricted to authors active throughout the study period, and the stayer confidence interval excludes within-author effects even a tenth the size of the full-sample estimate. The mechanism is most parsimoniously ecological (community-level discursive convergence) rather than individual-level AI adoption, though the data cannot cleanly separate this account from concurrent secular change.
Comments36 pages, 5 figures, 6 tables. Replication package available (code and instructions). Keywords: political discourse; semantic similarity; discourse homogenization; generative AI; computational text analysis
