@inproceedings{kellert-matlis-2022-social,
title = "Social Context and User Profiles of Linguistic Variation on a Micro Scale",
author = "Kellert, Olga and
Matlis, Nicholas Hill",
editor = {Scherrer, Yves and
Jauhiainen, Tommi and
Ljube{\v{s}}i{\'c}, Nikola and
Nakov, Preslav and
Tiedemann, J{\"o}rg and
Zampieri, Marcos},
booktitle = "Proceedings of the Ninth Workshop on NLP for Similar Languages, Varieties and Dialects",
month = oct,
year = "2022",
address = "Gyeongju, Republic of Korea",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.vardial-1.2/",
pages = "14--19",
abstract = "This paper presents a new tweet-based approach in geolinguistic analysis which combines geolocation, user IDs and textual features in order to identify patterns of linguistic variation on a sub-city scale. Sub-city variations can be connected to social drivers and thus open new opportunities for understanding the mechanisms of language variation and change. However, measuring linguistic variation on these scales is challenging due to the lack of highly-spatially-resolved data as well as to the daily movement or users' {\textquotedblleft}mobility{\textquotedblright} inside cities which can obscure the relation between the social context and linguistic variation. Here we demonstrate how combining geolocation with user IDs and textual analysis of tweets can yield information about the linguistic profiles of the users, the social context associated with specific locations and their connection to linguistic variation. We apply our methodology to analyze dialects in Buenos Aires and find evidence of socially-driven variation. Our methods will contribute to the identification of sociolinguistic patterns inside cities, which are valuable in social sciences and social services."
}
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<abstract>This paper presents a new tweet-based approach in geolinguistic analysis which combines geolocation, user IDs and textual features in order to identify patterns of linguistic variation on a sub-city scale. Sub-city variations can be connected to social drivers and thus open new opportunities for understanding the mechanisms of language variation and change. However, measuring linguistic variation on these scales is challenging due to the lack of highly-spatially-resolved data as well as to the daily movement or users’ “mobility” inside cities which can obscure the relation between the social context and linguistic variation. Here we demonstrate how combining geolocation with user IDs and textual analysis of tweets can yield information about the linguistic profiles of the users, the social context associated with specific locations and their connection to linguistic variation. We apply our methodology to analyze dialects in Buenos Aires and find evidence of socially-driven variation. Our methods will contribute to the identification of sociolinguistic patterns inside cities, which are valuable in social sciences and social services.</abstract>
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%0 Conference Proceedings
%T Social Context and User Profiles of Linguistic Variation on a Micro Scale
%A Kellert, Olga
%A Matlis, Nicholas Hill
%Y Scherrer, Yves
%Y Jauhiainen, Tommi
%Y Ljubešić, Nikola
%Y Nakov, Preslav
%Y Tiedemann, Jörg
%Y Zampieri, Marcos
%S Proceedings of the Ninth Workshop on NLP for Similar Languages, Varieties and Dialects
%D 2022
%8 October
%I Association for Computational Linguistics
%C Gyeongju, Republic of Korea
%F kellert-matlis-2022-social
%X This paper presents a new tweet-based approach in geolinguistic analysis which combines geolocation, user IDs and textual features in order to identify patterns of linguistic variation on a sub-city scale. Sub-city variations can be connected to social drivers and thus open new opportunities for understanding the mechanisms of language variation and change. However, measuring linguistic variation on these scales is challenging due to the lack of highly-spatially-resolved data as well as to the daily movement or users’ “mobility” inside cities which can obscure the relation between the social context and linguistic variation. Here we demonstrate how combining geolocation with user IDs and textual analysis of tweets can yield information about the linguistic profiles of the users, the social context associated with specific locations and their connection to linguistic variation. We apply our methodology to analyze dialects in Buenos Aires and find evidence of socially-driven variation. Our methods will contribute to the identification of sociolinguistic patterns inside cities, which are valuable in social sciences and social services.
%U https://aclanthology.org/2022.vardial-1.2/
%P 14-19
Markdown (Informal)
[Social Context and User Profiles of Linguistic Variation on a Micro Scale](https://aclanthology.org/2022.vardial-1.2/) (Kellert & Matlis, VarDial 2022)
ACL