Abstract
The aim of this study was to investigate the hotspots of WeChat official accounts and the impact of their pushes on user information behaviour including reading rate, sharing rate, number of comments or collections and fan growth rate. Using nine official accounts provided by the Sootoo Network, this study collected data on more than 10,000 pushes released from January to December 2017. In this study, a second-order user information behaviour model using the collected data was constructed. Based on empirical research, a prediction model of user information behaviour was built using a backpropagation neural network and random forest algorithm, and two variable sets were used for training. Then, the effect of different prediction models was analysed to determine the main factors affecting user information behaviour. This study addresses gaps in the field of WeChat research, and the results are of great practical significance for the operators of WeChat official accounts: they can help them optimise operation effects and enhance the influence of official accounts.
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