Gender Inference of Twitter Users in Non-English Contexts

By Morgane Ciot, Morgan Sonderegger, Derek Ruths. 2013

147 citations recorded by the source corpus.

While much work has considered the problem of latent attribute inference for users of social media such as Twitter, little has been done on non-English-based content and users. Here, we conduct the first assessment of latent attribute inference in languages beyond English, focusing on gender inference. We find that the gender inference problem in quite diverse languages can be addressed using existing machinery. Further, accuracy gains can be made by taking language-specific features into account. We identify languages with complex orthography, such as Japanese, as difficult for existing methods, suggesting a valuable direction for future research.

Subfields: Artificial Intelligence

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Citations in the corpus

Demographic Factors Improve Classification Performance (2015, methodology): “, 2014a; Eisenstein, to appear), or b) used linguistic features to infer socio-economic attributes (Rosenthal and McKeown, 2011; Nguyen et al., 2011; Alowibdi et al., 2013; Ciot et al., 2013; Liu and Ruths, 2013; Bergsma et al., 2013; Volkova et al., 2015).”
Organizations Are Users Too: Characterizing and Detecting the Presence of Organizations on Twitter (2015, background): “…the organizationperson distinction, much work has been done on other specific aspects such as political affiliation (Cohen and Ruths 2013), gender (Ciot, Sonderegger, and Ruths 2013; Alowibdi , Buy, and Yu 2013), age (Nguyen, Smith, and Rosé 2011; Nguyen et al. 2013), location (Jurgens 2013), or…”
Semantic Variation in Online Communities of Practice (2018, unknown): “…a substantial amount of work has used NLP techniques to study correlations between linguistic variables and macro-sociological categories such as age (Nguyen et al., 2013), gender (Nguyen et al., 2014; Burger et al., 2011; Ciot et al., 2013), and other demographic factors (Eisenstein et al., 2014).”
Multitask Learning for Mental Health Conditions with Limited Social Media Data (2017, background): “…predict author characteristics such as age (Goswami et al., 2009; Rosenthal and McKeown, 2011; Nguyen et al., 2014), gender (Sarawgi et al., 2011; Ciot et al., 2013; Liu and Ruths, 2013; Volkova et al., 2015; Hovy, 2015), personality (Schwartz et al., 2013; Volkova et al., 2014; Plank and Hovy,…”
Cross-lingual syntactic variation over age and gender (2015, background): “One line of research has focused on predictive models for age and gender (Alowibdi et al., 2013; Ciot et al., 2013) to add meta-data on Twitter, but again, error rates are too high for use in sociolinguistic hypothesis testing.”
Developing Age and Gender Predictive Lexica over Social Media (2014, methodology): “In this case, the lexica outperformed previous results for gender prediction of Twitter users, which ranged from 75.5% to 87% (Burger et al., 2011; Ciot et al., 2013; Liu and Ruths, 2013; Al Zamal et al., 2012).”
Gender Prediction for Authors of Russian Texts Using Regression And Classification Techniques (2016, background): “Most studies of the classification of texts according to the gender of their authors have been conducted using English texts and there have been only a few studies dealing with other languages (see [3] for details), especially Slavic ones.”
Computational Sociolinguistics: A Survey (2016, methodology): “By detecting such ‘je suis’ constructions, Ciot, Sonderegger, and Ruths (2013) improved performance of gender classification in French.”
Motivating Personality-aware Machine Translation (2015, background): “It has been shown that standard approaches to gender classification on English texts can be sub-optimal for non-English language data (Ciot et al., 2013).”
Semi-supervised Gender Classification with Joint Textual and Social Modeling (2016, methodology): “More recently, some studies focus on some specific application scenarios on supervised gender classification, such as multi-lingual gender classification (Ciot et al. , 2013; Alowibdi et al. , 2013) and interactive gender classification (Li et al. , 2015).”
User Classification with Multiple Textual Perspectives (2016, background): “More recently, some studies focus on some specific application scenarios on gender classification, such as multi-lingual gender classification (Ciot et al., 2013; Alowibdi et al., 2013), inferring gender by crowd (Nguyen et al., 2014) and interactive gender classification (Li et al., 2015).”