Social Network Personality Prediction Model Based on Multi-channel Information Fusion

Abstract

A social network personality prediction model with multi-channel information (MCIPP) is constructed. In the framework of in-depth learning, objective behavior data are used to automatically predict users’ personality traits, and whether users’ online behavior is consistent with their offline personality traits is analyzed. Specifically, the bi-directional long-term and short-term memory network (BiLSTM) and Attention mechanism (Attention) are used to capture the context semantic features of the text, and a syntactic dependency tree is constructed through a graph convolution network (GCN) to obtain a syntactic-based structural representation. Attention is integrated into a Topic Model to extract deep semantic information, and finally, the deep semantic information is input into a Softmax layer to obtain the personality tendency of a user Weibo. The results show that the MCIPP model has a good prediction effect with the highest accuracy of 0.806 4. There is a significant positive correlation between online and offline corresponding dimensions of individuals. Therefore, this model can be used to conduct psychological modeling on user network data, so that theory-driven psychological scientific research can objectively interpret individual psychology and behavior.

Keywords

• big five personality
• social network
• personality prediction
• deep learning
• multi-channel information