Understanding how the semantic features of contents influence the diffusion of government microblogs: Moderating role of content topics

Understanding users' behavior mechanism of information diffusion on government microblogs is helpful for formulating effective strategies to promote public participation. However, limited effort has been made to examine the effects of extensive textual features and their differences on different topics. Therefore, on the basis of the elaboration likelihood model, we develop a model to explain how the extensive semantic features will influence the diffusion of government microblog posts with different topics. A model test with real data from Sina Weibo demonstrates the promotional effect of positive words, city names, adjectives/adverbs, and dissimilar contents, and that negative words will hinder the diffusion. The effect of city names for political news is greater than that for living information, while the influence of positive words for living information is greater than that for political news. Contributions to the literature and practice are discussed.