Male, female, and nonbinary differences in UK Twitter self-descriptions: A fine-grained systematic exploration
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AbstractPurpose: Although gender identities influence how people present themselves on social media, previous studies have tested pre-specified dimensions of difference, potentially overlooking other differences and ignoring nonbinary users. Design/methodology/approach: Word association thematic analysis was used to systematically check for fine-grained statistically significant gender differences in Twitter profile descriptions between 409,487 UK-based female, male, and nonbinary users in 2020. A series of statistical tests systematically identified 1474 differences at the individual word level, and a follow up thematic analysis grouped these words into themes. Findings: The results reflect offline variations in interests and in jobs. They also show differences personal disclosures, as reflected by words, with females mentioning qualifications, relationships, pets, and illnesses much more, nonbinaries discussing sexuality more, and males declaring political and sports affiliations more. Other themes were internally imbalanced, including personal appearance (e.g., male: beardy; female: redhead), self-evaluations (e.g., male: legend; nonbinary: witch; female: feisty), and gender identity (e.g., male: dude; nonbinary: enby; female: queen). Research limitations: The methods are affected by linguistic styles and probably under-report nonbinary differences. Practical implications: The gender differences found may inform gender theory, and aid social web communicators and marketers. Originality/value: The results show a much wider range of gender expression differences than previously acknowledged for any social media site.
CitationThelwall, M., Thelwall, S. and Fairclough, R. (2021) Male, female, and nonbinary differences in UK Twitter self-descriptions: A fine-grained systematic exploration. Journal of Data and Information Science, 6(2), pp. 1-27. https://doi.org/10.2478/jdis-2021-0018
PublisherDe Gruyter Open
JournalJournal of Data and Information Science
Description© 2021 The Authors. Published by De Gruyter. This is an open access article available under a Creative Commons licence. The published version can be accessed at the following link on the publisher’s website: https://doi.org/10.2478/jdis-2021-0018
Except where otherwise noted, this item's license is described as https://creativecommons.org/licenses/by-nc-nd/4.0/