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Cited by 48Gender, Feminism, and MediaSocial Media and PoliticsEthics and Social Impacts of AI

Twitter's Glass Ceiling: The Effect of Perceived Gender on Online Visibility

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Nilizadeh, Shirin, Groggel, Anne, Lista, Peter, Das, Srijita, Ahn, Yong-Yeol, Kapadia, Apu, Rojas, Fabio·2021·Proceedings of the International AAAI Conference on Web and Social Media
DOI 10.1609/icwsm.v10i1.14711
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Abstract

Social media is a new public sphere where people can, in principle, communicate with each other regardless of their status. However, social categories like gender may still bias online communication, replicating offline disparities. Examining over 94,000 Twitter users, we investigate the association between perceived gender and measures of online visibility: how often Twitter users are followed, assigned to lists, and retweeted. Our analysis shows that users perceived as female experience a 'glass ceiling,' similar to the barrier women face in attaining higher positions in companies. For users in lower quartiles of visibility, being perceived as female is associated with more visibility; however, this tendency flips among the most visible users where being perceived as male is strongly associated with more visibility. Our results suggest that gender presented in social media profiles likely frame interactions as well as perpetuates old inequalities online.

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  • The way users are perceived affects their interaction patterns and visibility on a platform.

    “the way in which users are perceived affects their interaction patterns (e.g., the volume of shared content or of followees), as well as their visibility on a platform (e.g., how often they are followed, added to lists, or retweeted)”

    From Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019) · cites this paper as Nilizadeh et al., 2016

    The Twitter Activity section reports differences by perceived gender in tweet volume and followee counts, and the Introduction examines perceived gender in relation to followers, list additions, and retweets. These support an association with interaction patterns and visibility, but the paper does not establish that being perceived a certain way causes users to tweet more or follow more people; the Introduction says the relationship between demographics and user behavior is not its focus. (LLM, conf=0.85)

    Experimental — accuracy not yet measured

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Cited by

Sorted by citations

Authors
Nilizadeh, S., Groggel, A., Lista, P., Das, S., Ahn, Y.-Y., Kapadia, A., & Rojas, F.
Affiliations
  • University of California, Santa Barbara (US)
  • Indiana University Bloomington (US)
ORCID
Venue
Proceedings of the International AAAI Conference on Web and Social Media
Publisher
Association for the Advancement of Artificial Intelligence (AAAI)
Published
2021-08-04
Volume
10
Issue
1
Pages
289-298
ISSN
  • 2162-3449 (ISSN-L)
  • 2334-0770
DOI
10.1609/icwsm.v10i1.14711
OpenAlex
W2573963059
Cited by
48
References
2
Citations by year
2026: 3 · 2025: 1 · 2024: 2 · 2023: 2 · 2022: 17 · 2021: 4 · 2020: 3 · 2019: 2 · 2018: 4 · 2017: 10
FWCI
45.39
Type
conference paper
Language
en
Open access
Locations
  • Proceedings of the International AAAI Conference on Web and Social Media (publishedVersion)
  • AAAI Publications (The Association for the Advancement of Artificial Intelligence (AAAI)) (submittedVersion)
Funders
  • National Science Foundation (1252697)
  • Microsoft Research
  • Indiana University Bloomington
Topics
  • Social Sciences › Gender Studies › Gender, Feminism, and Media
  • Social Sciences › Communication › Social Media and Politics
  • Social Sciences › Safety Research › Ethics and Social Impacts of AI
Keywords
perceived gender, online visibility, Twitter, gender bias, follower count, retweets, lists, gender stereotypes

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