Demographic Representation and Collective Storytelling in the Me Too Twitter Hashtag Activism Movement
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Abstract (arXiv)
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.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). First posted on arXiv 13 Oct 2020.
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Citing papers per year: 2021: 1; 2022: 2; 2023: 1; 2024: 1; 2025: 0; 2026: 1.
6 papers.
- 2022Then and Now: Quantifying the Longitudinal Validity of Self-Disclosed Depression Diagnosescited by 4
- 2022What are People Talking about in #BlackLivesMatter and #StopAsianHate? Exploring and Categorizing Twitter Topics Emerging in Online Social Movements through the Latent Dirichlet Allocation Modelcited by 2
- 2023Social World Knowledge: Modeling and Applicationscited by 1
- 2024The Role of Network and Identity in the Diffusion of Hashtagscited by 1
- 2021SocialVec: Social Entity Embeddingscited by 0
- 2026Social Knowledge for Cross-Domain User Preference Modelingcited by 0
- 2026Social Knowledge for Cross-Domain User Preference Modeling10 Mar 2026
- 2024The Role of Network and Identity in the Diffusion of Hashtags17 Jul 2024
- 2023Social World Knowledge: Modeling and Applications28 Jun 2023
- 2022Then and Now: Quantifying the Longitudinal Validity of Self-Disclosed Depression Diagnoses22 Jun 2022
- 2022What are People Talking about in #BlackLivesMatter and #StopAsianHate? Exploring and Categorizing Twitter Topics Emerging in Online Social Movements through the Latent Dirichlet Allocation Model29 May 2022
- 2021SocialVec: Social Entity Embeddings5 Nov 2021