Paper Info
Title
Socioeconomic Dependencies of Linguistic Patterns in Twitter: a Multivariate Analysis.
Abstract
Our usage of language is not solely reliant on cognition but is arguably determined by myriad external factors leading to a global variability of linguistic patterns. This issue, which lies at the core of sociolinguistics and is backed by many small-scale studies on face-to-face communication, is addressed here by constructing a dataset combining the largest French Twitter corpus to date with detailed socioeconomic maps obtained from national census in France. We show how key linguistic variables measured in individual Twitter streams depend on factors like socioeconomic status, location, time, and the social network of individuals. We found that (i) people of higher socioeconomic status, active to a greater degree during the daytime, use a more standard language; (ii) the southern part of the country is more prone to use more standard language than the northern one, while locally the used variety or dialect is determined by the spatial distribution of socioeconomic status; and (iii) individuals connected in the social network are closer linguistically than disconnected ones, even after the effects of status homophily have been removed. Our results inform sociolinguistic theory and may inspire novel learning methods for the inference of socioeconomic status of people from the way they tweet.
Year
DOI
Venue
2018
10.1145/3178876.3186011
WWW '18: The Web Conference 2018 Lyon France April, 2018
Keywords
DocType
Volume
computational sociolinguistics, Twitter data, socioeconomic status inference, social network analysis, spatiotemporal data
Journal
abs/1804.01155
ISBN
Citations 
PageRank 
978-1-4503-5639-8
0
0.34
References 
Authors
11
5
Name
Order
Citations
PageRank
Jacob Levy Abitbol112.05
Márton Karsai242230.42
Jean-Philippe Magué300.34
Jean-Pierre Chevrot442.08
Eric Fleury525225.08