Rene Weber's research in the area of data science is focused on the extraction of complex latent content features (e.g., moral conflict, hate speech, argument strength, character relations) from both textual narratives (e.g., movie scripts, news articles, song lyrics, short stories) and audiovisual narratives (e.g., movies, public service announcements). His team develops new techniques for the combination of natural language processing tools with advanced human coding tasks and applies these techniques in large-scale, global datasets for the better understanding and prediction of communication phenomena such as moral outrage, polarization, (fake) news sharing, and news-event cycles.