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research-article

A little bird told me your gender: : Gender inferences in social media

Authors:

E. Fosch-Villaronga,

B.H.M. CustersAuthors Info & Claims

Volume 58, Issue 3

https://doi.org/10.1016/j.ipm.2021.102541

Published: 01 May 2021 Publication History

Highlights

•

Inferential analytics may reinforce existing biases about gender stereotyping.

•

In a N = 109 pilot sample, 19% of Twitter users were misgendered.

•

Gender classifiers are binary and do not account for other than male/female gender categories.

•

LGBTQAI+ community members and straight women may be more often misgendered than straight men in social media.

Abstract

Online and social media platforms employ automated recognition methods to presume user preferences, sensitive attributes such as race, gender, sexual orientation, and opinions. These opaque methods can predict behaviors for marketing purposes and influence behavior for profit, serving attention economics but also reinforcing existing biases such as gender stereotyping. Although two international human rights treaties include explicit obligations relating to harmful and wrongful stereotyping, these stereotypes persist online and offline. By identifying how inferential analytics may reinforce gender stereotyping and affect marginalized communities, opportunities for addressing these concerns and thereby increasing privacy, diversity, and inclusion online can be explored. This is important because misgendering reinforces gender stereotypes, accentuates gender binarism, undermines privacy and autonomy, and may cause feelings of rejection, impacting people's self-esteem, confidence, and authenticity. In turn, this may increase social stigmatization. This study brings into view concerns of discrimination and exacerbation of existing biases that online platforms continue to replicate and that literature starts to highlight. The implications of misgendering on Twitter are investigated to illustrate the impact of algorithmic bias on inadvertent privacy violations and reinforcement of social prejudices of gender through a multidisciplinary perspective, including legal, computer science, and critical feminist media-studies viewpoints. An online pilot survey was conducted to better understand how accurate Twitter's gender inferences of its users’ gender identities are. This served as a basis for exploring the implications of this social media practice.

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Information Processing and Management: an International Journal Volume 58, Issue 3

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https://dl.acm.org/doi/10.1145/3633071
Silva MMelo-Gomes LMoro M(2024)From words to genderInformation Processing and Management: an International Journal10.1016/j.ipm.2024.10364761:3Online publication date: 2-Jul-2024
https://dl.acm.org/doi/10.1016/j.ipm.2024.103647
Tanevska AChandra SBarbareschi GEguchi AHan ZKorpan ROstrowski APerugia GRavindranath SSeaborn KWinkle KCastellano GRiek LCakmak MLeite I(2023)Inclusive HRI IICompanion of the 2023 ACM/IEEE International Conference on Human-Robot Interaction10.1145/3568294.3579965(956-958)Online publication date: 13-Mar-2023
https://dl.acm.org/doi/10.1145/3568294.3579965
Winkle KMcMillan DArnelid MHarrison KBalaam MJohnson ELeite ICastellano GRiek LCakmak MLeite I(2023)Feminist Human-Robot InteractionProceedings of the 2023 ACM/IEEE International Conference on Human-Robot Interaction10.1145/3568162.3576973(72-82)Online publication date: 13-Mar-2023
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Baumgartner RArora PBath CBurljaev DCiereszko KCusters BDing JErnst WFosch-Villaronga EGalanos VGremsl THendl TKropp CLenk CMartin PMbelu SMorais dos Santos Bruss SNapiwodzka KNowak ERoxanne TSamerski SSchneeberger DTampe-Mai KVlantoni KWiggert KWilliams R(2023)Fair and equitable AI in biomedical research and healthcareArtificial Intelligence in Medicine10.1016/j.artmed.2023.102658144:COnline publication date: 1-Oct-2023
https://dl.acm.org/doi/10.1016/j.artmed.2023.102658
de Graaf MPerugia GFosch-Villaronga ELim ABroz FShort ENeerincx MSakamoto DWeiss AHiatt LShiomi M(2022)Inclusive HRIProceedings of the 2022 ACM/IEEE International Conference on Human-Robot Interaction10.5555/3523760.3523994(1247-1249)Online publication date: 7-Mar-2022
https://dl.acm.org/doi/10.5555/3523760.3523994
Benabdallah GAlexander AGhosh SGlogovac-Smith CJacoby LLustig CNguyen AParkhurst AReyes KTan NWolcher EPsarra ARosner D(2022)Slanted Speculations:Proceedings of the 2022 ACM Designing Interactive Systems Conference10.1145/3532106.3533449(85-99)Online publication date: 13-Jun-2022
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Andrus MVilleneuve S(2022)Demographic-Reliant Algorithmic Fairness: Characterizing the Risks of Demographic Data Collection in the Pursuit of FairnessProceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency10.1145/3531146.3533226(1709-1721)Online publication date: 21-Jun-2022
https://dl.acm.org/doi/10.1145/3531146.3533226
Gordon-Tapiero AWood ALigett KWeitzner DFeigenbaum JYoo C(2022)The Case for Establishing a Collective Perspective to Address the Harms of Platform PersonalizationProceedings of the 2022 Symposium on Computer Science and Law10.1145/3511265.3550450(119-130)Online publication date: 1-Nov-2022
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Pater JFiesler CZimmer M(2022)No Humans HereProceedings of the ACM on Human-Computer Interaction10.1145/34928576:GROUP(1-13)Online publication date: 14-Jan-2022
https://dl.acm.org/doi/10.1145/3492857
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