<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>In this paper, we present the methodology we have developed for the detection of lexical innovations, implemented here on a corpus of 650 million of French tweets covering a period from 2012 to 2019. Once detected, innovations are categorized as change or buzz according to whether their use has stabilized or dropped over time, and three phases of their dynamics are automatically identified. In order to validate our approach, we further analyse these dynamics by modelling the user network and characterising the speakers using these innovations via network variables. This allows us to propose preliminary observations on the role of individuals in the diffusion process of linguistic innovations which are in line with Milroy &amp; Milroy’s (1997) theories and encourage further investigations.</p>
</abstract>

In this paper, we present the methodology we have developed for the detection of lexical innovations, implemented here on a corpus of 650 million of French tweets covering a period from 2012 to 2019. Once detected, innovations are categorized as change or buzz according to whether their use has stabilized or dropped over time, and three phases of their dynamics are automatically identified. In order to validate our approach, we further analyse these dynamics by modelling the user network and characterising the speakers using these innovations via network variables. This allows us to propose preliminary observations on the role of individuals in the diffusion process of linguistic innovations which are in line with Milroy & Milroy’s (1997) theories and encourage further investigations.

Detecting and categorising lexical innovations in a corpus of tweets

ICAR laboratory (UMR 5191), École Normale Supérieure de Lyon

Psychology of Language and Communication

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

{"article-title":"Detecting and categorising lexical innovations in a corpus of tweets"}

In this paper, we present the methodology we have developed for the detection of lexical innovations, implemented here on a corpus of 650 million of French...

Detecting and categorising lexical innovations in a corpus of tweets

Pubblicato online: 21 ott 2022

Pagine: 313 - 329

DOI: https://doi.org/10.2478/plc-2022-15

Parole chiave
computational sociolinguistics, linguistic innovations, S-curve, Twitter, big data, network, diffusion of innovations

© 2022 Louise Tarrade et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Detecting and categorising lexical innovations in a corpus of tweets

Pubblicato online: 21 ott 2022

Pagine: 313 - 329

DOI: https://doi.org/10.2478/plc-2022-15

Parole chiavecomputational sociolinguistics, linguistic innovations, S-curve, Twitter, big data, network, diffusion of innovations

© 2022 Louise Tarrade et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Parole chiave
computational sociolinguistics, linguistic innovations, S-curve, Twitter, big data, network, diffusion of innovations