What Drives Deep Reader Engagement with Socially-Generated Content? The Critical Role of Dual-Layer Information

Abstract

Social media platforms have rapidly gained popularity due to features such as personalized recommendations and real-time push notifications, attracting a large number of creators to produce content. However, with the explosive growth of content and information overload, readers’ expectations for content and interaction quality have increased, making it increasingly difficult to engage them deeply. This poses management challenges to the platform’s ecosystem, such as high visitor volume but low retention rates. This study, leveraging the latest GPT technology, provides a more scientific approach to content metrics and a detailed exploration of interaction categories to examine the impact of information, authors, and interactions on reader engagement behavior. It particularly considers the effects of internal and external layers of social-generated content and different levels of reader engagement. Combining real community data, it is found that the impact of content information is the most crucial, with this impact being distributed across content and interaction aspects. It is suggested that social media platforms should strive to balance vivacity and informativeness when producing content. Additionally, the role of content, authors, and interactions varies across different product categories, so differentiated strategies should be employed accordingly.