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.Author summary: In everyday language, words are constantly being created, and these words either persist or disappear. Although this phenomenon has been the subject of much linguistic research, the factors which influence the fate of a new word remain largely unknown, partly because of the difficulty of recording spontaneous language use over time. Examining the varieties of language used on social media allows us to overcome these limitations. We collected over 650 million tweets written in French, covering several years of ordinary interactions between 2.5 million users. We also collected the network of social links between these users. We identified nearly 400 words that appeared in the corpus between 2012 and 2014, and tracked their diffusion over 5 years within the network of users. Some of these words lead to changes, while others generate only ephemeral buzz. By looking at the position in the network of users who adopt these innovations, we show that words adopted by users who are more central in their community and easily in contact with other communities become established in the language, and vice versa. Thus, the position in the network of speakers who adopt these words is enough to predict their fate.