Elucidating Public Sentiment Toward Chinese Platform Workers via LLM-driven Analysis of Weibo Data

Abstract

This thesis investigates public perceptions of four fast-growing platform-based occupations in China, including food delivery riders, rideshare drivers, e-commerce retailers, and internet influencers, using Weibo data from 2017 to 2024. This research uses attribute-based sentiment analysis (ABSA) to evaluate five occupational dimensions (i.e., economic returns, work environment, work autonomy, career stability, and occupational prestige) and links them to societal perceptions of warmth and competence. Using the Stereotype Content Model framework, the findings show that while rideshare drivers and internet influencers are perceived negatively in terms of warmth and competence, e-commerce retailers are viewed favorably on both dimensions. Meanwhile, food delivery riders are perceived as warm yet lacking competence. This study indicates that occupational prestige stands out as the most influential factor in shaping these perceptions. It also demonstrates that public sentiment can react either temporarily or permanently toward external disruptions, such as the COVID-19 pandemic. Further analysis reveals that the key determinants of whether perception shifts are transient or permanent depends on the degree of structural challenges faced by the occupation. Methodologically, this study introduces a novel and scalable framework that leverages large-language models (LLMs) to perform ABSA, offering more granular insights in both horizontal (cross-occupational) and vertical (within-occupation) dimensions. This interdisciplinary approach not only enriches our understanding of platform workers in China but also provides a replicable model applicable to various domains in computational social science that can greatly benefit policymakers, platforms, and scholars.