Misinformation Features Detection in Weibo: Unsupervised Learning, Latent Dirichlet Allocation, and Network Structure

Abstract

This study employs an unsupervised learning technique to identify features of misinformation on Weibo, a popular social media platform in China. By utilizing BERT (Bidirectional Encoder Representations from Transformers) for sequence classification, we were able to detect a dataset with a mix of true and false information, achieving a perfect recall rate for false information detection. However, the precision for true information was low, with a 3% precision, 6% recall, and an F1 score of 6%, indicating a high rate of misclassification. To address this, we conducted a Latent Dirichlet Allocation (LDA) analysis on the misclassified true information, identifying specific features that led to incorrect classification. Additionally, social network analysis revealed the presence of structural holes within the information network. This study contributes to the understanding of misinformation detection mechanisms and provides insights into the social dynamics of information spread.

Keywords

• Misinformation detection
• unsupervised learning
• BERT
• latent dirichlet allocation (LDA)
• social network analysis