Sharing unwanted sexual experiences online: A cross-platform analysis of disclosures before, during and after the #MeToo movement

Abstract

Online disclosure of sexual violence victimisation is a relatively new phenomenon. While prior research has mainly relied on analysis of Twitter data from the #MeToo period, this study compares such disclosures across platforms over two years. Using machine learning, 2927 disclosures were identified for quantitative content analysis and multiple correspondence analysis. Online platforms differed in timing of the posts, information shared, information density, co-occurrence of information and the length of the disclosure message. Most disclosures were found on the platform Twitter, and during the #MeToo movement. These posts differ from disclosures on other platforms and outside the viral movement. Regarding the content, across all platforms and periods, clustering was found around offender-oriented information, making the offender an explicit part of the experience. This study shows that an exclusive focus on online disclosures on Twitter and during viral movements gives a biased and incomplete picture of what online disclosure of sexual victimisation entails. Our cross-platform analysis over time allows for more universal statements about the content and context of online disclosures of sexual victimisation.