Revisiting Deep Learning-based Rumor Detection Models with Interpretable Tools
[Objective]This study explores whether content-based deep detection models can identify the semantics of rumors.[Methods]First,we use the BERT model to identify the key features of rumors from benchmark datasets in Chinese and English.Then,we utilized two interpretable tools,LIME,based on local surrogate models,and SHAP,based on cooperative game theory,to analyze whether these features can reflect the nature of rumors.[Results]The key features calculated by the interpretable tools on different models and datasets showed significant differences,and it is challenging to decide the semantic relationship between the features and rumors.[Limitations]The datasets and models examined in this study need to be expanded.[Conclusion]Deep learning-based rumor detection models only work with the features of the training set and lack sufficient generalization and interpretability for diverse real-world scenarios.