Exploring sustained self-disclosure mechanisms among social media users: an application of self-expansion theory

Purpose Self-disclosure, as a new type of user-generated content is increasingly becoming an important resource for the survival and development of social media. The study seeks to explore social media users' self-disclosure from a sustained and process-oriented perspective. Furthermore, the study aims to examine the moderating effect of privacy concern in the forming mechanism of sustained self-disclosure. Design/methodology/approach The study collected real interaction data from 820 users from a prominent content-sharing social media in China by employing web crawlers, and construct a longitudinal panel dataset spanning 16 months. We employed Panel Vector Autoregression (PVAR) and Impulse Response Functions (IRFs) to examine the forming mechanism of sustained self-disclosure and its time-varying effect among social media users. Findings The study reveals that the sustained self-disclosure is driven by two pathways, including identity-shaping and relationship-integration based on self-expansion theory. Past self-disclosure quantity and past self-disclosure sentiment positively influence future sustained self-disclosure. Meanwhile, past peer interaction sentiment shows a positive effect on both quantity and sentiment of future sustained self-disclosure, while past peer interactions quantity has no significant effect on future disclosure quantity. These effects show a trend of short-term growth followed by long-term decay. Furthermore, privacy concerns serve as a boundary condition to moderate these mechanisms, with low-privacy-concern users being more strongly influenced by the above factors. Originality/value The study complements the research on sustained self-disclosure by adopting a temporally dynamic, process-oriented perspective. The PVAR model provides a framework for the understanding of sustained self-disclosure and offers insights for platform management.