Automated personality prediction from social media is gaining increasing attention in natural language processing and social sciences communities. However, due to high labeling costs and privacy issues, the few publicly available datasets are of limited size and low topic diversity. We address this problem by introducing a large-scale dataset derived from Reddit, a source so far overlooked for personality prediction. The dataset is labeled with Myers-Briggs Type Indicators (MBTI) and comes with a rich set of features for more than 9k users. We carry out a preliminary feature analysis, revealing marked differences between the MBTI dimensions and poles. Furthermore, we use the dataset to train and evaluate benchmark personality prediction models, achieving macro F1-scores between 67% and 82% on the individual dimensions and 82% accuracy for exact or one-off accurate type prediction. These results are encouraging and comparable with the reliability of standardized tests.

Bipolar disorder, an illness characterized by manic and depressive episodes, affects more than 60 million people worldwide. We present a preliminary study on bipolar disorder prediction from user-generated text on Reddit, which relies on users' self-reported labels. Our benchmark classifiers for bipolar disorder prediction outperform the baselines and reach accuracy and F1-scores of above 86%. Feature analysis shows interesting differences in language use between users with bipolar disorders and the control group, including differences in the use of emotion-expressive words.

Personality and demographics are important variables in social sciences, whilein NLP they can aid in interpretability and removal of societal biases.However, datasets with both personality and demographic labels are scarce. Toaddress this, we present PANDORA, the first large-scale dataset of Reddit commentslabeled with three personality models (including the well-established Big 5 model) and demographics (age, gender, and location) for more than 10k users. Weshowcase the usefulness of this dataset on three experiments, where we leveragethe more readily available data from other personality models to predict theBig 5 traits, analyze gender classification biases arising frompsycho-demographic variables, and carry out a confirmatory and exploratoryanalysis based on psychological theories. Finally, we present benchmarkprediction models for all personality and demographic variables.

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