We use a deep bidirectional transformer to extract the Myers-Briggs personality type from user-generated data in a multi-label and multi-class classification setting. Our dataset is large and made up of three available personality datasets of various
social media platforms including Reddit, Twitter, and Personality Cafe forum. We induce personality embeddings from our transformer-based model and investigate if they can be used for downstream text classification tasks. Experimental evidence shows that personality embeddings are effective in three classification tasks including authorship verification, stance, and hyperpartisan detection. We also provide novel and interpretable analysis for the third task: hyperpartisan news classification.
