Riveter: Measuring Power and Social Dynamics Between Entities

Abstract

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.

Shortchanged: Uncovering and Analyzing Intimate Partner Financial Abuse in Consumer Complaints

“…Deep learning and natural language processing have been used to parse and analyze large datasets, which have enabled the inference of otherwise inaccessible characteristics [4,83,87]. Scholars have leveraged these approaches to elicit risk factors to vulnerable groups, contextualize taboo topic areas (e.g., end-of-life [111], climate change [38]), and identify under-recognized forms of sociotechnical harms [2,53].…”

“…We then investigated how these variables intersected, by charting and interpreting any patterns or connections between categories via qualitative analysis software Atlas.ti to gain a clearer understanding and explanation of the 'bigger picture' [22]. 4 Using a bespoke charting and summarization matrix (see [47,62]), two authors identified higher level categories and typologies, which are presented in our findings (Section 4).…”