Construction of the Social Network Information Dissemination Index System Based on CNNs

The information dissemination index system is an effective way to measure the dissemination of public opinion events in social networks. Due to the complexity, variability, and asymmetry of information, the construction of traditional information dissemination index systems demands excessive reliance on manual intervention, has large deviations, and is applied in a limited range. Such shortcomings cannot meet the requirements of constructing an objective, comprehensive, and highly credible index system. Therefore, we propose a method of constructing a multilevel and multigranular information dissemination index system with complex perspectives. In addition, we use the deep learning method of the convolutional neural network to extract the rich convolution features of public opinion events in the information dissemination process. Then, we train the weight, and it forms the corresponding weight of the information dissemination index systems. The experimental results prove that the method we use is superior to other methods and has better performance on the data set of a specific field.