Evidence map›Paper›PMID 35295189›Full record

ArticleProceedings of the ACM on human-computer interaction2021

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

Aaron Mueller, Zach Wood-Doughty, Silvio Amir, Mark Dredze, Alicia Lynn Nobles

Open access · greenAbstract read
In one paragraph

Article in Proceedings of the ACM on human-computer interaction, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
0.3field-weighted citation impact, top 39% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed, 3 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors at 2 institutions in 1 country.

Aaron MuellerJohns Hopkins University, USA.
Zach Wood-DoughtyJohns Hopkins University, USA.
Silvio AmirJohns Hopkins University, USA.
Mark DredzeJohns Hopkins University, USA.
Alicia Lynn NoblesUniversity of California San Diego, USA.
Johns Hopkins University · USUniversity of California, San Diego · US

Funding

Quantifying Treatment Help-Seeking Among Self-Identified Opioid Users on Social MediaK25DA049944 · NIDA · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI NOBLES, ALICIA · 2020 to 2021
$339k
NIDA NIH HHS K25 DA049944
6 · The paper itself

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 this work for digital activism research and design, including suggestions to raise visibility by those who were under-represented in this hashtag activism movement.

Indexed as

demographic inferencemetootopic modeling

Identifiers

PMID35295189
PMCPMC8920314
OpenAlexW3093209708

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.