The ‘me too’ movement originated to help survivors of sexual violence by providing resources and building a community of advocates to exemplify the magnitude of sexual violence victimization. This movement gained momentum via Twitter due to the viral hashtag—#metoo. YouTube is often used as a means of expression in younger generations, thus sexual violence survivors began using the platform as a way to disseminate ‘me too’ narratives. Therefore, this study aimed to examine how sexual violence narratives resulting from the ‘me too’ movement are being told on YouTube and understand the components of the narratives related to self-blaming mindsets. Based on predetermined search criteria, researchers identified and screened YouTube videos of people sharing ‘me too’ narratives, and developed themes and codes (e.g., type of violence, perpetrator characteristics). Descriptive statistics and a logistic regression were conducted using demographic, experience, and attitudinal data to predict self-blaming mindsets. Sixty-two YouTube videos were included, consisting of 96 individual ‘me too’ stories. The sample was mostly female, and perpetrators were predominately strangers. The model explained 19.3% of the variance in self-blaming attitudes. Odds of self-blaming rose 4.589 times for those who experienced sexual harassment, and 6.109 times for those who experienced rape. If the perpetrator was not mentioned in the video, odds of self-blaming dropped by 89.4%. This study suggests self-blaming beliefs are prominent among victims, even when they have the space to share their story. Overall, our findings support the continued need for further education and support for victims.
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Nelon, Jordan L.; Spadine, Mandy N.; Patterson, Meg S.; Brown, Sydney E.; Bookout, Christina L.; Woods, Lauren M.; and Fehr, Sara K.
"An Observational Analysis of ‘Me Too’ Narratives from YouTube,"
Health Behavior Research:
Domestic and Intimate Partner Violence Commons, Health Communication Commons, Public Health Education and Promotion Commons, Social Media Commons