Authenticity Classification of WeChat Group Chat Messages Based on LDA and NLP
Nihan Nie, Hengyi Guo, Wei Song · 2024
This study conducts an in-depth verification and analysis of the authenticity of information in WeChat group chats by integrating Latent Dirichlet Allocation (LDA) topic modeling with advanced Natural Language Processing (NLP) techniques such as XLNet and BERT. Leveraging LDA, the thematic structure of group chat content is revealed, and through the integration of NLP technologies like XLNet and BERT, a comprehensive analysis of the information is achieved. Experimental results demonstrate that our developed model performs exceptionally well in identifying the authenticity of information, confirming the effectiveness of this method in the domain of social media information verification. This research not only deepens our understanding of the authenticity of information in WeChat group chats but also provides a more effective tool for social media platforms to detect and prevent the spread of false information. It opens up a new perspective on social media information authentication research and points out future research directions.