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      Demographic Representation and Collective Storytelling in the Me Too Twitter Hashtag Activism Movement

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          Abstract

          The #MeToo movement on Twitter has drawn attention to the pervasive nature of sexual harassment and violence. While #MeToo has been praised for providing support for self-disclosures of harassment or violence and shifting societal response, it has also been criticized for exemplifying how women of color have been discounted for their historical contributions to and excluded from feminist movements. Through an analysis of over 600,000 tweets from over 256,000 unique users, we examine online #MeToo conversations across gender and racial/ethnic identities and the topics that each demographic emphasized. We found that tweets authored by white women were overrepresented in the movement compared to other demographics, aligning with criticism of unequal representation. We found that intersected identities contributed differing narratives to frame the movement, co-opted the movement to raise visibility in parallel ongoing movements, employed the same hashtags both critically and supportively, and revived and created new hashtags in response to pivotal moments. Notably, tweets authored by black women often expressed emotional support and were critical about differential treatment in the justice system and by police. In comparison, tweets authored by white women and men often highlighted sexual harassment and violence by public figures and weaved in more general political discussions. We discuss the implications of work for digital activism research and design including suggestions to raise visibility by those who were under-represented in this hashtag activism movement. Content warning: this article discusses issues of sexual harassment and violence.

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          Author and article information

          Journal
          13 October 2020
          Article
          2010.06472
          b1875c17-4061-4cea-a364-01c1d7733d8d

          http://arxiv.org/licenses/nonexclusive-distrib/1.0/

          History
          Custom metadata
          27 pages (incl. 5 for references). Submitted to CSCW 2021
          cs.CL

          Theoretical computer science
          Theoretical computer science

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