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

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 dis-closures were found on the platform Twitter, and during the #MeToo movement. These posts differ from dis-closures 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 move-ments 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 dis-closures of sexual victimisation.

Automated summary

Online disclosure of sexual violence victimisation is a new phenomenon. This study compares disclosures across platforms over two years, finding differences in timing, information shared, and content. The exclusive focus on Twitter and viral movements gives a biased picture, while a cross-platform analysis allows for more universal statements about online disclosures of sexual victimisation in terms of content and context.