TY - GEN
T1 - Socioeconomic dependencies of linguistic patterns in twitter
T2 - 27th International World Wide Web, WWW 2018
AU - Abitbol, Jacob Levy
AU - Karsai, Márton
AU - Magué, Jean Philippe
AU - Chevrot, Jean Pierre
AU - Fleury, Eric
N1 - Publisher Copyright:
© 2018 IW3C2 (International World Wide Web Conference Committee), published under Creative Commons CC BY 4.0 License.
PY - 2018/4/10
Y1 - 2018/4/10
N2 - 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.
AB - 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.
KW - Computational sociolinguistics
KW - Social network analysis
KW - Socioeconomic status inference
KW - Spatiotemporal data
KW - Twitter data
UR - http://www.scopus.com/inward/record.url?scp=85085187578&partnerID=8YFLogxK
U2 - 10.1145/3178876.3186011
DO - 10.1145/3178876.3186011
M3 - Conference contribution
AN - SCOPUS:85085187578
T3 - The Web Conference 2018 - Proceedings of the World Wide Web Conference, WWW 2018
SP - 1125
EP - 1134
BT - The Web Conference 2018 - Proceedings of the World Wide Web Conference, WWW 2018
PB - Association for Computing Machinery, Inc
Y2 - 23 April 2018 through 27 April 2018
ER -