An Analysis of the Status and Influencing Factors of Perceived Social Support Among Adolescents: Based on Weibo Big Data

Abstract: [Objective] This study aimed to explore a Weibo-based approach to assessing adolescents’ perceived social support and to examine its demographic variations.
[Methods] Approximately 300,000 Weibo posts from adolescents aged 12–18 across 31 provinces in China were collected via Python. A perceived social support lexicon was developed encompassing four dimensions—emotional, informational, instrumental, and appraisal support. Word frequency analysis was conducted using the validated Chinese text analysis software Wenxin System, followed by multidimensional statistical analyses based on gender, age, and region.
[Results] The constructed lexicon consisted of 1,312 validated entries. Significant differences in perceived social support were found across demographic groups: adolescents in western and northeastern regions showed lower levels than those in eastern and central regions, and males scored slightly higher than females.
[Limitations] Data were limited to publicly available Weibo texts, potentially affecting sample representativeness. In addition, the lexicon method may not fully capture deeper psychological semantics.
[Conclusions] Social media text analysis provides an effective tool for evaluating perceived social support among adolescents and offers practical value for early psychological risk detection.