Can the adoption of health information on social media be predicted by information characteristics?

Purpose This paper aims to explore the relationship between the characteristics of social media health information and its adoption. The purpose is to identify information characteristics that can be used to estimate the level of health information adoption in advance. Design/methodology/approach According to the Information Adoption Model (IAM), the study extracted ten information characteristics from the aspects of information quality and information source credibility. The sample data was collected from the top ten influential health accounts based on the Impact List of Sina Weibo to test the effectiveness of these characteristics in distinguishing information at different levels of adoption. The forecasting of information adoption level is regarded as a binary classification question in the study and support vector machine (SVM) is used to do the research. Findings The results indicate that ten information characteristics chosen in this study are related to information adoption. Based on these information characteristics, it is feasible to estimate the level of health information adoption, and the estimation accuracy is relatively high. Originality/value A lot of work has been done in previous researches to reveal the factors that influence information adoption. The theoretical contribution of this work is to further discuss how to use the influencing factors to do some predictive work for information adoption. In practice, it will help health information publishers to disseminate high-quality health information more effectively as well as promote the adoption of health information.