Abstract: We present a study of the relationship between gender, linguistic style, and social networks, using a novel corpus of 14,000 Twitter users. Prior quantitative work on gender often treats this social variable as a female/male binary; we argue for a more nuanced approach. By clustering Twitter users, we find a natural decomposition of the dataset into various styles and topical interests. Many clusters have strong gender orientations, but their use of linguistic resources sometimes directly conflicts with the population-level language statistics. We view these clusters as a more accurate reflection of the multifaceted nature of gendered language styles. Previous corpus-based work has also had little to say about individuals whose linguistic styles defy population-level gender patterns. To identify such individuals, we train a statistical classifier, and measure the classifier confidence for each individual in the dataset. Examining individuals whose language does not match the classifier's model for their gender, we find that they have social networks that include significantly fewer same-gender social connections, and that in general, social network homophily is correlated with the use of same-gender language markers. Pairing computational methods and social theory thus offers a new perspective on how gender emerges as individuals position themselves relative to audiences, topics, and mainstream gender norms. [ In this paper, we present a study of the relationship between gender, language, and social network connections in social media text. We use a novel corpus of more than 14,000 individuals on the microblog site Twitter, and perform a computational analysis of the impact of gender on both their lexical choices and their social networks. We address two limitations of previous quantitative analyses of language and gender. First, previous work has focused on words that distinguish women and men solely by gender. This disregards theoretical arguments and qualitative evidence that gender can be enacted through a diversity of styles and stances. Using clustering, we identify a range of linguistic styles and topical interests among the authors in our dataset. Many of these clusters have strong gender orientations -demonstrating the strong relationship between language and
REF present a study of the relationship between gender, language, and social network connections in social media text.
Gender identity and lexical variation in social media
Journal of Sociolinguistics 18 (2014) 135-160
Sociology Computer Science