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 ...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), selfevaluations(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 underreport 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.展开更多
文摘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), selfevaluations(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 underreport 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.
