A prediction model of users' attention transfer in the context of multitopic competition

Purpose Multiple topics often exist on social media platforms that compete for users' attention. To explore how users’ attention transfers in the context of multitopic competition can help us understand the development pattern of the public attention. Design/methodology/approach This study proposes the prediction model for the attention transfer behavior of social media users in the context of multitopic competition and reveals the important influencing factors of users' attention transfer. Microblogging features are selected from the dimensions of users, time, topics and competitiveness. The microblogging posts on eight topic categories from Sina Weibo, the most popular microblogging platform in China, are used for empirical analysis. A novel indicator named transfer tendency of a feature value is proposed to identify the important factors for attention transfer. Findings The accuracy of the prediction model based on Light GBM reaches 91%. It is found that user features are the most important for the attention transfer of microblogging users among all the features. The conditions of attention transfer in all aspects are also revealed. Originality/value The findings can help governments and enterprises understand the competition mechanism among multiple topics and improve their ability to cope with public opinions in the complex environment.