Reddit self-declarations offer auditable labels for demographic inference

Researchers have built a framework for inferring Reddit users’ age, gender and partisan affiliation that relies on an unusually transparent source of supervision: the users themselves. In a study published in Scientific Reports, Federico Cinus, Corrado Monti, Paolo Bajardi and Gianmarco De Francisci Morales used more than 850,000 public self-declarations, in which Reddit users explicitly state these attributes about themselves, as training and validation labels for sociodemographic inference models.

The motivation is a persistent problem in computational social science. Demographic labels for social media users are typically produced through researcher-defined proxies or opaque modeling assumptions, which makes the resulting measurements hard to audit. When such inferred attributes are then used to interpret population-level patterns, errors and biases in the labeling pipeline can propagate silently into the conclusions. By grounding supervision in user-provided statements rather than proxy heuristics, the new framework makes the labeling step explicit and inspectable.

Methodologically, the team trained models on sparse subreddit participation signals, the pattern of which communities a user posts in or engages with. They compared simple probabilistic classifiers, notably Multinomial Naive Bayes, against embedding-based baselines that are commonly used in Reddit research. The evaluation covered two task types: coarse binary classification of individual users and aggregate quantification, where the goal is to estimate the proportion of a group with a given attribute rather than label each user.

The headline result is that the simpler model often wins. Across these tasks, Multinomial Naive Bayes frequently outperformed more complex alternatives. Relative to the unsupervised state-of-the-art embedding method, the Naive Bayes approach improved ROC AUC by up to 19 percent. Under true supervision, it maintained quantification error below 15 percent for both gender and year of birth, indicating that aggregate demographic estimates can be produced with reasonably tight error bounds when the labels themselves are trustworthy.

Beyond raw accuracy, the authors emphasize interpretability. The models produce calibrated outputs, meaning their predicted probabilities can be treated as meaningful uncertainty estimates, and they support subreddit-level feature importance analysis. In practice, this means researchers can examine which communities’ participation patterns drive a particular demographic inference, rather than accepting a black-box score. That transparency matters especially when inferred attributes feed into population-level analyses of online communities.

The study’s scope and limitations are worth noting. The work concerns Reddit, a platform where public self-declarations happen to be plentiful, and the reported performance figures apply to the coarse binary and aggregate tasks the authors tested, not to fine-grained multiclass prediction. The paper is published open access, and the version shared at publication is citable under a permanent DOI while remaining subject to final editorial edits in the Version of Record.

For the field, the results position self-declarations as an explicit and auditable supervision source for studying both user-level and population-level sociodemographic patterns on Reddit.

Subject of Research: Sociodemographic inference on Reddit using public user self-declarations as transparent training labels

Article Title: Transparent sociodemographic inference on reddit from self-declarations

Article References: Cinus, F., Monti, C., Bajardi, P., & De Francisci Morales, G. (2026). Transparent sociodemographic inference on reddit from self-declarations. Scientific Reports. https://doi.org/10.1038/s41598-026-75155-2

Image Credits: AI Generated

DOI: 10.1038/s41598-026-75155-2

Keywords: Reddit, sociodemographic inference, self-declarations, computational social science, Multinomial Naive Bayes, embeddings, quantification, calibration, auditability, gender, age, partisan affiliation