Information Propagation Prediction and Specific Information Suppression in Social Networks

In recent years, with the increasing number of users in social networks such as Twitter, Facebook and Sina Weibo, the amount of information has rapidly expanded. The spread of untrue information hidden in massive information has brought adverse effects. How to regulate or suppress the spread of specific information is a technical challenge faced by network information management. In order to solve this problem, first of all, the independent information forwarding prediction mechanism based on machine learning method, which does not depend on the propagation model is proposed, so as to predict the information propagation. Secondly, based on the independent cascade model, considering the particularity of the scenario in this paper, the asynchronous information unequal competition propagation model is proposed as the competitive propagation mechanism of specific information and immune information. Finally, three selection algorithms of seed nodes are proposed and the immune information is widely spread in the network by injecting immune information into the seed nodes, so as to suppress the spread of specific information. Experiments based on real social network data show that the information propagation prediction model and the selection algorithms of seed nodes proposed have good effects on the regulation and suppression of specific information propagation.