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Cited by 46Social Media and PoliticsEthics and Social Impacts of AIRace, History, and American Society

More than a Modern Day Green Book: Exploring the Online Community of Black Twitter

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Klassen, Shamika, Kingsley, Sara, McCall, Kalyn, Weinberg, Joy, Fiesler, Casey·2021·Proceedings of the ACM on Human-Computer Interaction
DOI 10.1145/3479602
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Abstract

The Negro Motorist Green Book was a tool used by the Black community to navigate systemic racism throughout the U.S. and around the world. Whether providing its users with safer roads to take or businesses that were welcoming to Black patrons, The Negro Motorist Green Book fostered pride and created a physical network of safe spaces within the Black community. Building a bridge between this artifact which served Black people for thirty years and the current moment, we explore Black Twitter as an online space where the Black community navigates identity, activism, racism, and more. Through interviews with people who engage with Black Twitter, we surface the benefits (such as community building, empowerment, and activism) and challenges (like dealing with racism, appropriation, and outsiders) on the platform, juxtaposing the Green Book as a historical artifact and Black Twitter as its contemporary counterpart. Equipped with these insights, we make suggestions including audience segmentation, privacy controls, and involving historically disenfranchised perspectives into the technological design process. These proposals have implications for the design of technologies that would serve Black communities by amplifying Black voices and bolstering work toward justice.

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  • Black Twitter is a crucial online platform that shapes cultural production, political activism, and educational exchange within the global Black community.

    “Black Twitter, now operating on X (formerly Twitter), is a crucial online platform that shapes cultural production, political activism, and educational exchange within the global Black community.”

    From Exploring #Diasporawars on Black Twitter (2025) · cites this paper as Klassen et al., 2021

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  • Exploring #Diasporawars on Black Twitter

    Musgrave, T., Ye, Y., Toyama, K., Schoenebeck, S., & Threats, M. (2025) · Social Media + Society · doi:10.1177/20563051251352834 · Cited by 3 · see citing works

    “Research has extensively explored its impact on community dynamics (Klassen et al., 2021), cultural production (A. Brock, 2012), democratic participation (Jackson, 2016), consumer behavior (Brouard et al., 2023), and identity formation (Maragh, 2018).”

Authors
Klassen, S., Kingsley, S., McCall, K., Weinberg, J., & Fiesler, C.
Affiliations
  • University of Colorado Boulder (US)
  • Carnegie Mellon University (US)
  • Harvard University (US)
ORCID
Venue
Proceedings of the ACM on Human-Computer Interaction
Publisher
Association for Computing Machinery (ACM)
Published
2021-10-13
Volume
5
Issue
CSCW2
Pages
1-29
ISSN
2573-0142 (ISSN-L)
DOI
10.1145/3479602
OpenAlex
W3206175027
Cited by
46
References
12
Citations by year
2026: 8 · 2025: 9 · 2024: 14 · 2023: 8 · 2022: 7
FWCI
9.58
Type
journal article
Language
en
Open access
Licence
www.acm.org/publications/policies/copyright_policy#Background (vor, 5 d)
Locations
Proceedings of the ACM on Human-Computer Interaction (publishedVersion)
Funders
National Science Foundation (1704303)
Topics
  • Social Sciences › Communication › Social Media and Politics
  • Social Sciences › Safety Research › Ethics and Social Impacts of AI
  • Social Sciences › Sociology and Political Science › Race, History, and American Society
Keywords
Black Twitter, online communities, Black identity, racism, activism, appropriations, community building, empowerment, social media design, audience segmentation, privacy control, inclusive design, safe spaces

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

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