Disproportionate Removals and Differing Content Moderation Experiences for Conservative, Transgender, and Black Social Media Users: Marginalization and Moderation Gray Areas

Honorable Mention

Social media sites use content moderation to attempt to cultivate safe spaces with accurate information for their users. However, content moderation decisions may not be applied equally for all types of users, and may lead to disproportionate censorship related to people's genders, races, or political orientations. We conducted a mixed methods study involving qualitative and quantitative analysis of survey data to understand which types of social media users have content and accounts removed more frequently than others, what types of content and accounts are removed, and how content removed may differ between groups. We found that three groups of social media users in our dataset experienced content and account removals more often than others: political conservatives, transgender people, and Black people. However, the types of content removed from each group varied substantially. Conservative participants' removed content included content that was offensive or allegedly so, misinformation, Covid-related, adult, or hate speech. Transgender participants' content was often removed as adult despite following site guidelines, critical of a dominant group (e.g., men, white people), or specifically related to transgender or queer issues. Black participants' removed content was frequently related to racial justice or racism. More broadly, conservative participants' removals often involved harmful content removed according to site guidelines to create safe spaces with accurate information, while transgender and Black participants' removals often involved content related to expressing their marginalized identities that was removed despite following site policies or fell into content moderation gray areas. We discuss potential ways forward to make content moderation more equitable for marginalized social media users, such as embracing and designing specifically for content moderation gray areas.

Quick Actions

Share

Share this page

ios_share

https://hci.top/en/papers/cscw/64131/2021

AdRecommended

Learn AI Coding at CodeNow

open_in_newOpen DOI Link
DOI: https://doi.org/10.1145/3479610
At a Glance

Paper Snapshot

fact_check
dataset
Source
CSCW
calendar_month
Year
2021
emoji_events
Award
Honorable Mention
group
Authors
4 authors
sell
Subtopics
—
work
Professions
—
article
Content Status
Abstract only
hub
Related Papers
0 related papers