Exploring meaning in life from social network content in the sleep scenario.

Introduction: The exploration of life's meaning has been a key topic across disciplines, and artificial intelligence is now beginning to investigate it. Methods: This study leveraged social media to assess meaning in life (MIL) and its associated factors at individual and group levels. We compiled a diverse dataset consisting of microblog posts (N = 7,588,597) and responses from user surveys (N = 448), annotated using a combination of self-assessment, expert opinions, and ChatGPT-generated insights. Our methodology examined MIL in three ways: (1) developing deep learning models to assess MIL components, (2) applying semantic dependency graph algorithms to identify MIL associated factors, and (3) constructing eight subnetworks to analyze factors, their interrelations, and MIL differences. Results: We validated these methods and bridged two foundational MIL theories, highlighting their interconnections. Discussion: By identifying psychological risk factors, our work may provide clues to mental health issues and inform possible intervention.