Social media platforms have transformed social interaction, fostering participatory culture and introducing new status markers based on metrics like follower counts. However, this has also led to a digital divide, where active participants gain privilege while others are excluded. Traditional explanations focus on the reproduction of social structures, emphasizing structural inequalities in access, skills, and support. This framework is insufficient in the context of platform societies, where technological features like algorithmic prediction and quantified attention create digital capital, influencing social mobility and challenging the conventional understanding of the digital divide. China provides a compelling case study with its rapid internet growth and booming platform industries, yet significant stratification persists. This article proposes a socio-technical approach, arguing that the digital divide stems from the concept of affordance—the interaction between technological properties and human agency within the platform environment. It emphasizes the mutual construction of society and technology, recognizing the significant role of digital identities and digital capital in shaping life chances. The study analyzes data from Sina Weibo users to explore how platform affordance shapes stratified uses and outcomes, comparing the explanatory power of structural and socio-technical factors.

The digital divide is typically categorized into three levels: the access gap (connectivity), the skills gap (digital literacy and usage), and the outcomes gap (benefits derived from ICT use). These levels are evident in both Western and developing contexts, including China, where a dualistic pattern exists between urban-rural and eastern-western regions. The literature highlights the unequal distribution of digital capital, defined as a combination of externalized resources and internalized abilities, with visible metrics like follower counts symbolizing social identity and economic benefits. While existing research acknowledges the interplay between digital divides and structural inequalities like gender, age, education, and income, it lacks a comprehensive understanding of the dynamics within platform environments. Social media platforms often evade institutional regulation and social responsibility, creating uncertainties about how they contribute to the digital divide despite fostering participatory culture.

This study utilizes survey data from 903 Sina Weibo users in China, collected through internet-based questionnaires in collaboration with Hanyi Big Data. The sample was designed to reflect the demographic characteristics of Chinese netizens and Weibo users, with quotas set for gender, age, and education. The study examines four categories of Sina Weibo functions (basic, simplified, multimedia, and topic functions) to analyze platform affordance. Technology-efficacy was measured by assessing the perceived difficulty of finding icon locations and understanding operation methods of these functions. Self-efficacy was measured by assessing users' perceived proficiency and the perceived value of these functions. Stratified uses were operationalized using online activity (creation or not, creation frequency, creation diversity). Stratified outcomes were measured by the number of Weibo followers. Demographic variables (gender, age, education, occupation, income) and internet experience were included as control variables. Multiple linear regression was used to test the relationship between technology-efficacy and self-efficacy. Marginal effects analyzed the moderating effects of demographic characteristics. Logistic, ordered, and Poisson regression models compared the relative influence of sociodemographic factors and socio-technical factors (platform affordance) on stratified uses. Ordered logistic regression examined the relationship between usage patterns and the accumulation of digital capital (follower count).

The analysis revealed a positive sequential relationship between technology-efficacy and self-efficacy (H1 supported), indicating that users' perception of a function's accessibility and usability positively influences their perceived capabilities and needs to use it. However, the moderating effect of personal and positional characteristics on this relationship was partially supported (H2 partially supported), with gender, age, and education showing significant effects, while social position did not. Specifically, lower technology-efficacy correlated with heightened gender inequality; generational gaps were significant between younger and older users; and educational differences narrowed as technology-efficacy increased. Platform affordance significantly influenced stratified uses (H3a supported), surpassing the influence of structural factors alone. Users with positive perceptions of platform functions and high self-efficacy were more likely to create content, and more frequently and diversely. Platform affordance and usage patterns jointly contributed to the stratification of digital capital (H3b supported), with frequent content creators and those using a wider range of functions accumulating more followers. While self-efficacy initially appeared influential in determining follower count, this effect diminished when controlling for creation frequency and diversity. The models incorporating platform affordance and usage patterns showed better fit than those relying solely on structural mechanisms.

The findings support the proposed socio-technical framework, demonstrating that platform affordance plays a crucial role in shaping the digital divide. The sequential relationship between technology-efficacy and self-efficacy highlights the importance of user perception in technology adoption. The moderating effects of demographic characteristics reveal how structural inequalities persist within the platform environment, but also demonstrate how individuals can adapt and strategically connect technology-efficacy and self-efficacy to overcome some of these disadvantages. The study's findings challenge traditional digital divide research by showing that platform affordance generates new forms of inequality through gradated action possibilities, influencing usage patterns and the distribution of digital capital. The crucial role of usage patterns in accumulating digital capital emphasizes the importance of active online participation in accessing various forms of capital and converting resources into tangible or intangible benefits.

This study contributes to the understanding of the digital divide by highlighting the role of platform affordance as a mechanism shaping stratified uses and unequal distribution of digital capital. It reveals the importance of user perception, the persistence of structural inequalities, and the influence of active participation in accumulating digital capital. Future research should use longitudinal data to investigate the long-term impacts of platform affordance on inequality, explore the role of algorithmic mechanisms in shaping affordances, and conduct comparative analyses across different platforms.

The cross-sectional nature of the data limits causal inferences. Self-reported data may be subject to biases. The study focuses on Sina Weibo, limiting generalizability to other platforms. Algorithmic mechanisms were not directly examined, limiting the depth of analysis regarding how technology actively shapes inequality.