Sociodemographic information provides critical context for interpreting digital behavior and its societal implications in many computational social science settings. However, the labels used to train and validate current inference methods frequently rely on researcher-defined proxies or opaque modeling assumptions. This can make demographic measurement difficult to audit, especially when inferred attributes are used to interpret population-level patterns.
Here, we study sociodemographic inference on Reddit using over 850 000 public user self-declarations of age, gender, and partisan affiliation as an explicit source of supervision. We train models on sparse subreddit participation signals and compare simple probabilistic models with embedding-based baselines commonly used for Reddit analysis. Across coarse binary classification and aggregate quantification tasks, Multinomial Naive Bayes (NB) often performs better than more complex alternatives.
Relative to the unsupervised state-of-the-art embedding method, NB improves ROC AUC by up to \(19\%\). Under true supervision, it maintains quantification error below \(15\%\) for gender and year of birth. The models produce calibrated and interpretable outputs, thus supporting uncertainty estimation and subreddit-level feature importance analysis.
These results position self-declarations as an explicit and auditable source of supervision for studying both user-level and population-level sociodemographic patterns on Reddit. By grounding labels in user-provided statements, the framework reduces reliance on researcher-selected proxies and supports transparent aggregate analyses of online communities. This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.
AI Research, Intesa Sanpaolo, Turin, Italy Humanities Department, University of Turin, Turin, Italy AI Lab, Intesa Sanpaolo Innovation Center, Turin, Italy AI Compliance, Intesa Sanpaolo, Turin, Italy The authors declare that they have no competing interests. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material.
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