Computational Sociolinguistics: A Survey

By Dong Nguyen, A. Seza Doğruöz, Carolyn Penstein Rosé, Franciska de Jong. 2016

205 citations recorded by the source corpus.

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 used 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.

Subfields: Artificial Intelligence, Gender Studies, Language and Linguistics

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Citations in the corpus

Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019, background): “…popular tasks, such as user classiﬁcation, trending topics detection, language identiﬁcation, or content ﬁltering (Cohen and Ruths, 2013; Olteanu et al., 2014a; Blodgett et al., 2016; Nguyen et al., 2016), and may also impact users’ exposure to a variety of information types (Nikolov et al., 2015).”
Style Transfer Through Back-Translation (2018, background): “Much work in computational social science has shown that people’s personal and demographic characteristics—either publicly observable (e.g., age, gender) or private (e.g., religion, political affiliation)—are revealed in their linguistic choices (Nguyen et al., 2016).”
Predicting Authorship and Author Traits from Keystroke Dynamics (2018, unknown): “Language is a social phenomenon (Nguyen et al., 2015).”
The Importance of Modeling Social Factors of Language: Theory and Practice (2021, background): “…reaching a point where this idea can become implemented in systems, it is a message that bears repeating in the NLP community (see also Hovy (2018) and Flek (2020) for similar points, as well as Nguyen et al. (2016) for an overview of the closely related issue of computational sociolinguistics).”
Gender Bias in Machine Translation (2021, background): “, 2015), it should be kept in mind that they are far from universal7 and should not be intended in a stereotyped and oversimplified manner (Bergvall et al., 1996; Nguyen et al., 2016; Koolen and van Cranenburgh, 2017).”
Can Large Language Models Transform Computational Social Science? (2023, methodology): “Computational sociolinguists use computational tools to measure the interactions between society and language, including the stylistic and structural features that distinguish speakers (Nguyen et al., 2016).”
On learning and representing social meaning in NLP: a sociolinguistic perspective (2021, background): “in NLP (Nguyen et al., 2016).”
Voice Interfaces in Everyday Life (2018, background): “Despite the wealth of enabling research in computational linguistics such as natural language processing, dialogue systems, and computational sociolinguistics [21], research that empirically examines the social and interactional issues of VUIs in everyday use is lacking.”
Does It Capture STEL? A Modular, Similarity-based Linguistic Style Evaluation Framework (2021, background): “A speaker’s style can, for example, be influenced by the situation, the speaker’s choices, and/or the speaker’s identity (Preoţiuc-Pietro et al., 2016; Flekova et al., 2016; Nguyen et al., 2016; Bell, 1984).”
Neural Unsupervised Domain Adaptation in NLP—A Survey (2020, background): “…of work on how human factors impact data in a covert or overt way, e.g., on how latent socio-demographic factors impact NLP performance (Hovy, 2015; Nguyen et al., 2016), or how direct data collection strategies like crowdsourcing impact corpus composition (Geva et al., 2019) or frequency effects…”
A Critical Review of Online Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2018, background): “Other resources we recommend: books [29, 33], talks and tutorials [1, 9, 15, 36], and several overview papers [4, 25, 31, 35].”