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Gender identity and lexical variation in social media ...
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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 ... : submission version ...
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Keyword:
Computation and Language cs.CL; FOS Computer and information sciences
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URL: https://dx.doi.org/10.48550/arxiv.1210.4567 https://arxiv.org/abs/1210.4567
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43 |
Discovering Sociolinguistic Associations with Structured Sparsity ...
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44 |
A Latent Variable Model for Geographic Lexical Variation ...
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45 |
A Latent Variable Model for Geographic Lexical Variation ...
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48 |
Part-of-Speech Tagging for Twitter: Annotation, Features, and Experiments
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In: DTIC (2010)
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49 |
Adding More Languages Improves Unsupervised Multilingual Part-of-Speech Tagging: A Bayesian Non-Parametric Approach
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In: MIT web domain (2009)
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Multilingual Part-of-Speech Tagging Two Unsupervised Approaches
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In: JAIR (2009)
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51 |
Gesture in automatic discourse processing ; Structured models of gesture for discourse processing
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