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‘Gender in Twitter: Styles, Stances, and Social Networks’ by David Bamman, Jacob Eisenstein, & Tyler Schnoebelen (2014)

Published onApr 03, 2020
‘Gender in Twitter: Styles, Stances, and Social Networks’ by David Bamman, Jacob Eisenstein, & Tyler Schnoebelen (2014)
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Bamman, D., Eisenstein, J., Schnoebelen, T.: Gender in Twitter: Styles, Stances and Social Networks. In: Journal of Sociolinguistics, 18, pp 135-160 (2014) 

URL: https://arxiv.org/abs/1210.4567v2




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.


Critical Annotation


Bamman et al., recognizing the tendency of gender inference models to adopt binary notions of gender and oversimplified assumptions about its relationship with language, propose an alternative more “nuanced” gender inference model. They combine computational methods with social theory, examining the social network of 14,464 Twitter users and utilize and a text-based gender classifier that achieves an 88% accuracy in gender prediction. They also correlate the output of their classification model with the gender composition of the Twitter user’s network. Through this, they determine that gender homophily correlates with the use of gendered language (i.e. the more gendered a user’s language, the more gendered their social network). Furthermore, Individuals whose gender is classified incorrectly have social networks that are much less homophilous than those of the individuals that the classifier gets right. Significantly, the authors don’t see misclassified individuals as outliers but rather as individuals who do gender differently, and a such influencing their linguistic choices and social behaviour. They suggest that there are multiple gendered styles and that the performance of popular gender norms in language is part of a gendered persona that shapes individual interactions.However, they are adamant, that the addition of social network information does not improve gender classification as these outliers are not the result of statistical aberration, but rather, indicate individuals who have adopted a persona different then larger norms. They suggest that it is this persona shapes their social network connection just as it shaped their linguistic resources.

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