PassivePy: A Tool to Automatically Identify Passive Voice in Big Text Data

The academic study of grammatical voice (e.g., active and passive voice) has a long history in the social sciences. Passive voice, for example, has been used to identify victim blaming in traumatic events, false versus truthful speech patterns, and levels of construal. Most evaluations of passive voice are experimental or small-scale field studies, however, and perhaps one reason for its lack of adoption is the difficulty associated with obtaining valid, reliable, and replicable results through automated means. In this paper, we introduce an automated tool to identify passive voice from large-scale text data, PassivePy. With minimal computational overhead, this package achieves 97% agreement with human coded data for grammatical voice as revealed in two large validation studies. In this paper, we discuss why passive voice is an important social and psychological construct, how PassivePy works, and conclude with pathways to apply this package in everyday psychological research.