Riveter: Measuring Power and Social Dynamics Between Entities
This tool improves accessibility to verb lexica for researchers studying social dynamics like gender bias, but it is incremental as it builds on existing lexical frameworks.
The authors tackled the problem of analyzing verb connotations for entities in text corpora, which previously required NLP skills, by developing Riveter, a complete pipeline that prepopulates connotation frames and provides scores and visualizations, making it accessible for researchers in computational social science and digital humanities.
Riveter provides a complete easy-to-use pipeline for analyzing verb connotations associated with entities in text corpora. We prepopulate the package with connotation frames of sentiment, power, and agency, which have demonstrated usefulness for capturing social phenomena, such as gender bias, in a broad range of corpora. For decades, lexical frameworks have been foundational tools in computational social science, digital humanities, and natural language processing, facilitating multifaceted analysis of text corpora. But working with verb-centric lexica specifically requires natural language processing skills, reducing their accessibility to other researchers. By organizing the language processing pipeline, providing complete lexicon scores and visualizations for all entities in a corpus, and providing functionality for users to target specific research questions, Riveter greatly improves the accessibility of verb lexica and can facilitate a broad range of future research.