How position in the network determines the fate of lexical innovations on Twitter

This study analyzes the diffusion of lexical innovations on Twitter to understand how the social network position of adopters impacts their success. Looking at both successful and failed neologisms, we categorize them into "changes" which become established and "buzzes" which decline over time. Using a corpus of 650 million French tweets, we reconstruct user networks and characterize adopters of innovations during different diffusion phases based on prestige, centrality, clustering, and external ties. In the early innovation phase, change and buzz adopters have similar peripheral profiles. During propagation, changes spread to prestigious, central individuals while buzzes do not, which predicts their eventual success or failure. By the establishment phase, changes reach highly central users with closer external ties. The results align with sociolinguistic theories about weak ties for innovation and strong ties for establishment. Additionally, logistic regression models based on early adopter profiles can predict the fate of innovations. This work sheds light on the diffusion dynamics of online lexical innovations and the crucial role of user network factors.

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Publication Details

Journal
PLOS complex systems.
Published
2024-09-03
DOI
https://doi.org/10.1371/journal.pcsy.0000005
Citations
1
Primary Topic
Complex Network Analysis Techniques
Type
article
Field-Weighted Citation Impact
0.28

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article

How position in the network determines the fate of lexical innovations on Twitter

Jean-Philippe Magué, Louise Tarrade, Jean‐Pierre Chevrot
1 citations
PLOS complex systems.
Complex Network Analysis Techniques
0.28
article

How position in the network determines the fate of lexical innovations on Twitter

Jean-Philippe Magué, Louise Tarrade, Jean‐Pierre Chevrot
article en
1 citations

Abstract

This study analyzes the diffusion of lexical innovations on Twitter to understand how the social network position of adopters impacts their success. Looking at both successful and failed neologisms, we categorize them into "changes" which become established and "buzzes" which decline over time. Using a corpus of 650 million French tweets, we reconstruct user networks and characterize adopters of innovations during different diffusion phases based on prestige, centrality, clustering, and external ties. In the early innovation phase, change and buzz adopters have similar peripheral profiles. During propagation, changes spread to prestigious, central individuals while buzzes do not, which predicts their eventual success or failure. By the establishment phase, changes reach highly central users with closer external ties. The results align with sociolinguistic theories about weak ties for innovation and strong ties for establishment. Additionally, logistic regression models based on early adopter profiles can predict the fate of innovations. This work sheds light on the diffusion dynamics of online lexical innovations and the crucial role of user network factors.

PLOS complex systems.Vol. 1(1)
École Normale Supérieure de Lyon (FR), Interactions, Corpus, Apprentissages, Représentations (FR), Laboratoire de Linguistique et Didactique des Langues Etrangères et Maternelles (FR), Université Grenoble Alpes (FR)
Université Grenoble Alpes, Agence Nationale de la Recherche, École Normale Supérieure de Lyon
Industry, innovation and infrastructure
Openalex Percentile: Top 46%
Complex Network Analysis Techniques
0.28
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How position in the network determines the fate of lexical innovations on Twitter — Jean-Philippe Magué, Louise Tarrade, et al. · PLOS complex systems. (2024) | TGRS Research Map | TGRS