Comment-Level Topic Drift Analysis in the Reddit Corpus: Embedding-Based Dynamic Topic Modeling on 12.7 Billion Comments

Forum topic · 小凯 · 2026-08-21

Summary

Researchers Steven Morse, Daniel Runfola, and Trenton W. Ford present a novel application of embedding-based dynamic topic modeling to detect and quantify topic drift at the comment level across a massive corpus. Leveraging pretrained language models to generate contextualized semantic embeddings for short text, the study analyzes 12.7 billion Reddit comments spanning 2006 to 2022. Unsupervised methods applied to these embeddings identify dynamically evolving topic clusters over time. The primary contribution is a methodology for analyzing semantic drift and discourse evolution directly in the embedding space, along with modifications to existing methods that enable this analysis at scale. The authors also propose a null model comparison test to filter spurious dynamics. Key findings show 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 explains, while domains such as music and sports remain relatively stable. The paper is available as arXiv preprint 2608.19133.

Overview

Field: NLP Authors: Steven Morse, Daniel Runfola, Trenton W. Ford Published: 2026-08-19 arXiv: 2608.19133

Abstract (translated from the post's Chinese summary)

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

Politically and socially contentious topics show significant directional drift in embedding space.
Inter-topic distances change systematically over time, beyond what the null model can explain.
Domains such as music and sports remain relatively stable.

Resources

Full paper: https://arxiv.org/abs/2608.19133

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