2024/10/31 by Dominic Sobhani, Amir Feder, Sobhani, Dominic +3
Decision Sciences · Social Sciences · #Big Data Technologies and Applications #Computation and Language (cs.CL) #Computational and Text Analysis Methods #FOS: Computer and information sciences #Human Mobility and Location-Based Analysis
paper · pdf · doi:10.48550/arxiv.2410.24126
openalex publication_date 2024/10/31 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Probabilistic topic models are a powerful tool for extracting latent themes from large text datasets. In many text datasets, we also observe per-document covariates (e.g., source, style, political affiliation) that act as environments that modulate a "global" (environment-agnostic) topic representation. Accurately learning these representations is important for prediction on new documents in unseen environments and for estimating the causal effect of topics on real-world outcomes. To this end, we introduce the Multi-environment Topic Model (MTM), an unsupervised probabilistic model that separates global and environment-specific terms. Through experimentation on various political content, from ads to tweets and speeches, we show that the MTM produces interpretable global topics with distinct environment-specific words. On multi-environment data, the MTM outperforms strong baselines in and out-of-distribution. It also enables the discovery of accurate causal effects.