Sharing textual content in the form of public posts on online platforms remains a significant part of the social web. Research on stylometric profiling suggests that despite users’ discreetness, and even under the guise of anonymity, the content and style of such posts may still reveal detailed author information. Studying how this might be inferred and obscured is relevant not only to the domain of cybersecurity, but also to those studying bias of classifiers drawing features from web corpora. While the collection of gold standard data is expensive, prior work shows that distant labels (i.e., those gathered via heuristics) offer an effective alternative. Currently, however, pre-existing corpora are limited in scope (e.g., variety of attributes and size). We present the SOBR corpus: 235M Reddit posts for which we used subreddits, flairs, and self-reports as distant labels for author attributes (age, gender, nationality, personality, and political leaning). In addition to detailing the data collection pipeline and sampling strategy, we report corpus statistics and provide a discussion on the various tasks and research avenues to be pursued using this resource. Along with the raw corpus, we provide sampled splits of the data, and suggest baselines for stylometric profiling. We close our work with a detailed set of ethical considerations relevant to the proposed lines of research.

Language models such as GPT-3 have caused a furore in the research community. Some studies found that GPT-3 has some creative abilities and makes mistakes that are on par with human behaviour. This paper answers a related question: Who is GPT-3? We administered two validated measurement tools to GPT-3 to assess its personality, the values it holds and its self-reported demographics. Our results show that GPT-3 scores similarly to human samples in terms of personality and - when provided with a model response memory - in terms of the values it holds. We provide the first evidence of psychological assessment of the GPT-3 model and thereby add to our understanding of this language model. We close with suggestions for future research that moves social science closer to language models and vice versa.