A Generative Multi-agent Framework for Modeling Depressive Language Entrainment

When people interact, they adjust their language to each other. This process of lexical entrainment fosters social agreeableness, but may also create jargon, hypes, memes, and even political polarization when communities converge on a shared vernacular. Here we study what happens when people with depression interact with each other in online social networks with varying degrees of lexical entrainment. We connect generative AI agents in a social network, each endowed with a personal mental health profile. The agents exchange Tweet-like messages that are shaped by their individual score on a PHQ-9 depression questionnaire, while the exchanges induce lexical entrainment. We find that cognitive distortions, a style of thinking associated with and possibly causative of internalizing disorders, can rapidly diffuse in social networks through the process of lexical entrainment, creating a depressogenic psycho-social environment that may lead to worse mental health outcomes throughout the community. Our results may inform targeted approaches to remove risk factors associated with social media use and mitigate the effects of lexical entrainment in communities with mental health challenges.