A robust gender inference model for online social networks and its application to LinkedIn and Twitter

Kokkos, Athanasios; Tzouramanis, Theodoros

doi:10.5210/fm.v19i9.5216

Cited by 22 publications

(16 citation statements)

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“…The dataset has been used in various subsequent studies (Bergsma and Van Durme 2013;Van Durme 2012;Volkova, Wilson, and Yarowsky 2013). Others created their own Twitter dataset (Eisenstein, Smith, and Xing 2011;Kokkos and Tzouramanis 2014;Liao et al 2014;Rao et al 2010;Zamal, Liu, and Ruths 2012). While early studies focused on English, recent studies have used Twitter data written in other languages as well, like Dutch (Nguyen et al 2013), Spanish and Russian (Volkova, Wilson, and Yarowsky 2013), and Japanese, Indonesian, Turkish, and French (Ciot, Sonderegger, and Ruths 2013).…”

Section: Data Sourcesmentioning

confidence: 99%

Computational Sociolinguistics: A Survey

Nguyen

Doğruöz

Rosé

et al. 2016

Computational Linguistics

176

143

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Language is a social phenomenon and variation is inherent to its social nature. Recently, there has been a surge of interest within the computational linguistics (CL) community in the social dimension of language. In this article we present a survey of the emerging field of 'Computational Sociolinguistics' that reflects this increased interest. We aim to provide a comprehensive overview of CL research on sociolinguistic themes, featuring topics such as the relation between language and social identity, language use in social interaction and multilingual communication. Moreover, we demonstrate the potential for synergy between the research communities involved, by showing how the large-scale data-driven methods that are widely used in CL can complement existing sociolinguistic studies, and how sociolinguistics can inform and challenge the methods and assumptions employed in CL studies. We hope to convey the possible benefits of a closer collaboration between the two communities and conclude with a discussion of open challenges.

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Section: Data Sourcesmentioning

confidence: 99%

Computational Sociolinguistics: A Survey

Nguyen

Doğruöz

Rosé

et al. 2016

Computational Linguistics

176

143

View full text Add to dashboard Cite

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“…For example, Felbo et al and Sarraute et al predict the gender with respectively 79.7% and 77.1% 3 accuracy, either by harnessing their temporal information using deep learning or by using linear SVM and logistic regression [5], [26]. Other works tackle the gender prediction problem in a similar way using different kinds of data sets, such as Twitter or LinkedIn data, the first name of a person or even chat texts [31]- [33]. Closer to our work, Al Zamal et al exploit the homophily in a Twitter network to predict the users' gender, age, and political affiliation [2], by analyzing how the knowledge of the data from some immediate friends of a given user can improve the prediction quality.…”

Section: Resultsmentioning

confidence: 99%

Improving individual predictions using social networks assortativity

Mulders

Bodt

Bjelland

et al. 2017

2017 12th International Workshop on Self-Organizing Maps and Learning Vector Quantization, Clustering and Data Visualization (W

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Abstract-Social networks are known to be assortative with respect to many attributes, such as age, weight, wealth, level of education, ethnicity and gender. This can be explained by influences and homophilies. Independently of its origin, this assortativity gives us information about each node given its neighbors. Assortativity can thus be used to improve individual predictions in a broad range of situations, when data are missing or inaccurate. This paper presents a general framework based on probabilistic graphical models to exploit social network structures for improving individual predictions of node attributes. Using this framework, we quantify the assortativity range leading to an accuracy gain in several situations. We finally show how specific characteristics of the network can improve performances further. For instance, the gender assortativity in real-world mobile phone data changes significantly according to some communication attributes. In this case, individual predictions with 75% accuracy are improved by up to 3%.

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“…Research has shown how valuable the social science lens is to computational fields (Kokkos & Tzouramanis, 2014;Nguyen, Doğruöz, Rosé, & de Jong, 2016;Otterbacher, 2013). Researchers at this intersection are aware of the tension between the theoretical framing and empirical methods of their work.…”

Section: Unlikely Alliesmentioning

confidence: 99%

“Condescending, Rude, Assholes”: Framing gender and hostility on Stack Overflow

Brooke¹

2019

Proceedings of the Third Workshop on Abusive Language Online

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The disciplines of Gender Studies and Data Science are incompatible. This is conventional wisdom, supported by how many computational studies simplify gender into an immutable binary categorization that appears crude to the critical social researcher. I argue that the characterization of gender norms is context specific and may prove valuable in constructing useful models. I show how gender can be framed in computational studies as a stylized repetition of acts mediated by a social structure, and not a possessed biological category. By conducting a review of existing work, I show how gender should be explored in multiplicity in computational research through clustering techniques, and layout how this is being achieved in a study in progress on gender hostility on Stack Overflow.

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A robust gender inference model for online social networks and its application to LinkedIn and Twitter

Cited by 22 publications

References 0 publications

Computational Sociolinguistics: A Survey

Computational Sociolinguistics: A Survey

Improving individual predictions using social networks assortativity

“Condescending, Rude, Assholes”: Framing gender and hostility on Stack Overflow

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