Kamile Hamiloğlu
Marmara University, Turkey; https://orcid.org/0000-0001-5094-8383
Buket Demirbüken
Marmara University, Turkey; https://orcid.org/0000-0001-7607-5381
DOI: https://doi.org/10.36534/erlj.2025.02.01
Bibliographic citation: (ISSN 2657-9774) Educational Role of Language Journal. Volume 2025-2(14). THE ACTIVE DIMENSION OF LANGUAGE AND OF LINGUISTIC EDUCATION, pp. 24-54.
Abstract
This study explores and compares lexical variations on Twitter and X (formerly Twitter) as social networks, based on gender and gender homophily. Data were collected at ten-year intervals from the same platform. The first dataset was collected in 2015, with 400 tweets (200 female, 200 male) randomly selected using Wamp Server. Gender classification was confirmed manually and via test data. Tweets were analysed through categories adapted from Bamman (2014), including named entities, taboo and swear words, numbers, emotional terms, emoticons, kinship terms, abbreviations, hashtags, and pronounceable/non-pronounceable words, along with emerging categories such as political and romantic words. Frequencies were examined, and clusters were formed based on lexical similarity to analyse language independent of gender stereotypes. Gender homophily was investigated by examining the gender composition of twenty networks using binomial distribution. In 2025, data were recollected; due to restricted access, 200 tweets were manually compiled. Findings suggest that language use may be gendered within the dataset; however, digital language differs from real-life use. Female authors tended to use more political, emotional, and interactional language, while male authors used more romantic expressions. Female tweets also included more swear and taboo words, as well as more hashtags. Male networks exhibited gender homophily, whereas female networks did not. When 2015 and 2025 data were compared, several patterns reversed, particularly in hashtags and romantic words, with increased use by male and female authors respectively. Political words showed a more balanced distribution. Gender homophily patterns also shifted, with no clear evidence of homophily in 2025.
Keywords: Twitter, X, gender, lexical variations, gender, homophily, digital/virtual environment, sociolinguistics
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