Driving factors of social media article diffusion: Empirical evidence from WeChat

Abstract

This study constructs three logistic models to predict whether an article on social media can achieve a high reading volume (100,000+), a high liking volume (above 1,099), and a high reading and liking volume simultaneously. Our findings are based on the analysis of 22,632 articles published by WeChat Official Accounts. The accuracy of these three models reaches 79%, 81.14%, and 84.26%, respectively. The models verify the important impact of emotions, news values, WeChat Official Account subscriber volume, page position, and publication time. They possess practical operability and reference value in predicting the communication effects of articles on social media. This study provides guiding principles for news production practice, regarding how to improve communication effects. This will help media organizations better allocate resources and achieve better communication outcomes. This study finds that the diffusion effects of news texts can be quantified and predicted, which lays a theoretical foundation for the future development of automated software for evaluating article communication effects on social media. In addition, this study has important implications for exploring how emotional arousal drives information diffusion on social media, emphasizing that emotion is a significant variable that cannot be ignored in news diffusion.