Voices of rape: Cognitive networks link passive voice usage to psychological distress in online narratives

Past studies of sexual assault have found that passive voice descriptions of rape elicit an increased perception of victim responsibility compared to active voice narratives (Bohner, 2001), contributing to victim blaming and the perpetuation of rape myths. Building on this, we investigate the relationship between passive/active voice usage and perception, but from the perspective of rape survivors as disclosed in their online rape narratives. We collect rape narratives from Reddit's r/sexualassault board and group them into a passive voice group and an active voice group. We detect differences between the two groups of text using a cognitive network science approach that creates network representations from text such that nodes represent words/concepts while links represent syntactic and semantic relationships between them. We systematically identify nodes that are significantly more central to one network compared to the other, thus identifying characteristic concepts that semantically differentiate the two groups of narratives. We then investigate the contexts of these concepts applying semantic frame analysis. We find that concepts related to psychological distress (e.g. PTSD, flashback) are significantly more central to passive voice narratives, providing quantitative evidence of a link between passive voice usage and an increased focus on psychological distress. We also find that family members (e.g. parent, brother) are more central to active voice narratives, suggesting a connection between active voice usage and an increased focus on others' roles in rape survivors' experiences. Our quantitative results reveal an important link between language and mental health that has valuable implications for therapeutic interventions. •Passive voice rape narratives place a greater focus on psychological distress•Active voice rape narratives place a greater focus on family members•Textual forma mentis networks create network representations from text•Cognitive network science is useful for detecting subtle differences in text•Semantic frame subgraphs provide insight into the contexts of key concepts