---
title: "基于嵌入的动态主题建模：Reddit 语料库评论级话题漂移分析"
source: "arXiv AI/CL/LG（论文）"
source_tier: "t2"
url: "https://arxiv.org/abs/2608.19133v1"
permalink: "https://feed.astralor.com/items/itm_421fe9f4d457411eeb4b6f34"
published: "2026-08-19T17:27:25.000Z"
score: 76
channel: "ai"
tags: ["Reddit","动态主题建模","嵌入表示","话题漂移"]
---

# 基于嵌入的动态主题建模：Reddit 语料库评论级话题漂移分析

## AI 点评

> 这篇最值得看的地方是它把话题漂移分析落到评论级，且用零模型做了严格检验；对关注网络话语演化、舆情监测和平台治理的人来说，方法可以直接借鉴，而不只是看结论。

## 速览

该论文提出一种基于预训练语言模型嵌入的动态主题建模方法，在12.7亿条Reddit评论（2006-2022）上实现评论级话题漂移检测。通过无监督聚类追踪嵌入空间中的语义演化，并引入零模型比较检验过滤虚假动态。结果显示政治与社会争议话题的方向性漂移显著，其主题间距离随时间系统变化，而音乐、体育等领域相对稳定。该方法为大规模话语演化分析提供了可扩展方案。

## 关键点

- 提出基于嵌入的动态主题建模方法，首次在12.7亿条Reddit评论上做评论级话题漂移分析。
- 引入零模型对照检验，区分真实语义漂移与随机波动。
- 发现政治/社会争议话题漂移显著，音乐/体育类话题相对稳定。
- 方法可扩展至大规模短文本语料，用于研究话语演化。

## 全文

We present a novel application of embedding-based dynamic topic modeling techniques to detect and quantify topic drift at the comment level in a massive corpus. By leveraging pretrained language models to generate contextualized semantic embeddings for short text, we analyzed 12.7 billion Reddit comments spanning 2006 to 2022. Using unsupervised methods on these embeddings, we identify dynamically evolving topic clusters over time. Our primary contribution is a methodology for analysis of semantic drift and discourse evolution in the embedding space itself. We also demonstrate modifications to existing methods that enable this analysis at scale, and we propose and demonstrate a null model comparison test to filter spurious dynamics. Key findings suggest that politically and socially contentious topics exhibit significant directional drift in embedding space, with inter-topic distances changing systematically over time beyond what the null model can explain, whereas domains such as music and sports remain comparatively stable.

---

引用前请核对原文;本文由星际流动整理:https://feed.astralor.com/items/itm_421fe9f4d457411eeb4b6f34