<abstract xmlns="http://www.w3.org/1999/xhtml">

<sec><h3>Purpose</h3>
<p>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.</p>
</sec>
<sec><h3>Design/methodology/approach</h3>
<p>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 1,474 differences at the individual word level, and a follow up thematic analysis grouped these words into themes.</p>
</sec>
<sec><h3>Findings</h3>
<p>The results reflect offline variations in interests and in jobs. They also show differences in 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).</p>
</sec>
<sec><h3>Research limitations</h3>
<p>The methods are affected by linguistic styles and probably under-report nonbinary differences.</p>
</sec>
<sec><h3>Practical implications</h3>
<p>The gender differences found may inform gender theory, and aid social web communicators and marketers.</p>
</sec>
<sec><h3>Originality/value</h3>
<p>The results show a much wider range of gender expression differences than previously acknowledged for any social media site.</p>
</sec>
</abstract>

Purpose
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 1,474 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 in 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.

Male, Female, and Nonbinary Differences in UK Twitter Self-descriptions: A Fine-grained Systematic Exploration

Journal of Data and Information Science

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Purpose
Although gender identities influence how people present themselves on social media, previous studies have tested pre-specified dimensions of...

Male, Female, and Nonbinary Differences in UK Twitter Self-descriptions: A Fine-grained Systematic Exploration

Article Category: Research Paper

Published Online: Mar 08, 2021

Page range: 1 - 27

Received: Dec 23, 2020

Accepted: Feb 07, 2021

DOI: https://doi.org/10.2478/jdis-2021-0018

Keywords
Twitter, Online self-presentation, Gender, Nonbinary genders, Twitter bios

© 2021 Mike Thelwall et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Figure 1

Male, Female, and Nonbinary Differences in UK Twitter Self-descriptions: A Fine-grained Systematic Exploration

Article Category: Research Paper

Published Online: Mar 08, 2021

Page range: 1 - 27

Received: Dec 23, 2020

Accepted: Feb 07, 2021

DOI: https://doi.org/10.2478/jdis-2021-0018

KeywordsTwitter, Online self-presentation, Gender, Nonbinary genders, Twitter bios

© 2021 Mike Thelwall et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Figure 1

Keywords
Twitter, Online self-presentation, Gender, Nonbinary genders, Twitter bios