AI Insight
This study examined what influences social media users' perceptions of data fairness by surveying 589 WeChat users. The research found that awareness and positive attitudes toward algorithms, transparency in data collection and processing, perceived control over personal data, and privacy self-efficacy all contributed to higher perceived data fairness. Privacy concerns negatively affected fairness perceptions, while surprisingly, transparency about how data is used had no significant impact on users' sense of control.
Why it matters
The findings provide actionable guidance for social media platforms and policymakers on designing data governance practices that users perceive as fair. By identifying specific factors like algorithmic awareness and data transparency that shape fairness perceptions, this research offers a roadmap for building more trustworthy digital platforms and addressing growing public concerns about data exploitation.
Understand the Science
BackgroundData fairness has become an increasingly important issue in the digital economy. Although social media platforms extensively collect and utilize personal data, limited research has examined how users perceive data fairness and the factors shaping such perceptions.MethodsDrawing on the Theory of Planned Behavior and Privacy Calculus Theory, a research model was developed incorporating algorithmic awareness, algorithmic attitude, data transparency, perceived control, privacy self-efficacy, and privacy concerns. Survey data from 589 WeChat users were analyzed using partial least squares structural equation modeling.ResultsThe results showed that algorithmic awareness positively influenced algorithmic attitude, which subsequently enhanced perceived data fairness. Data collection transparency and data processing transparency positively affected perceived control, whereas data use transparency had no significant effect. Perceived control and privacy self-efficacy positively influenced perceived data fairness. Privacy self-efficacy also reduced privacy concerns, which negatively affected perceived data fairness.ConclusionThis study advances the emerging literature on data fairness by identifying the key factors shaping users’ perceptions of data fairness in social media. The findings provide new insights into the psychological mechanisms underlying fairness perceptions and offer practical implications for developing more transparent and user-centered data governance practices.