User Behavior Prediction of Social Hotspots Based on Multi-Message Interaction and Neural Networks

User Behavior Prediction of Social Hotspots Based on Multi-Message Interaction and Neural Networks

Authors- Anil Mothe, Satyam Kumar, Sampath Kumar

Abstract-Introduces a prediction model for user participation behavior in the context of public-opinion analysis on social hot topics. It emphasizes the importance of message diversity in shaping user behavior and addresses the complexity of interactions among multiple messages. The proposed model leverages a multi- message interaction influence-driving mechanism to enhance the accuracy of user participation behavior predictions. To accommodate the intricate behaviors of users in multi-message hotspots and the limitations of simple backpropagation neural networks, the study combines this mechanism with a backpropagation neural network (BPNN) to create a user participation behavior prediction model. Recognizing that multi- message interaction can lead to overfitting in the BPNN, the article employs a simulated annealing algorithm to optimize the network, further improving prediction accuracy. The model not only forecasts user participation behavior in real-world situations involving multiple message interactions but also quantifies the relationships between different messages on hot topics.

DOI: /10.61463/ijset.vol.13.issue1.157