Fighting Misinformation in Health News: DCNN-CapsNet for Cross-Domain Health Misinformation Detection

Abstract: Misinformation propagation poses a significant challenge in the digital information era, particularly during crises such as pandemics. Detecting and combating misinformation is crucial for ensuring public health and safety. In this study, we focus on cross-domain misinformation detection for health news by leveraging a Covid-19 dataset to train a model capable of identifying Monkeypox misinformation articles. We propose a novel hybrid model that combines the strengths of both Deep Convolutional Neural Network (DCNN) and Capsule Neural Network (CapsNet), referred to as DCNN-CapsNet. This model reaps the benefits of both DCNN and CapsNet as two different neural network architectures that have been used as classifiers. To enhance the learning process, we employ incremental fine-tuning, which enables our model to begin with dynamic, pre-trained word embeddings that are updated throughout the training phase. This approach ensures that the embeddings remain relevant to the specific task of misinformation detection, adapting to the nuances of the dataset. Additionally, we incorporate margin loss as a critical component of our architecture. Margin loss is designed to improve the model’s ability to learn discriminative features by maximizing intra-cluster similarity and inter-cluster dissimilarity. This loss function addresses the limitations of traditional cross-entropy loss by enforcing a margin between predicted outputs and decision boundaries, thus reducing class overlap and mitigating the risk of overfitting. By ensuring that the learned representations of “real” and “fake” news are sufficiently separated, margin loss enhances the model’s robustness in detecting misinformation across different domains. Our model outperforms several baseline models on a Covid-19 dataset, achieving a 97.34% accuracy and 97.29% F1-score. Furthermore, it surpasses baselines on a Monkeypox dataset, achieving a 96.03% accuracy and 95.75% F1-score. The cross-domain evaluation, training on Covid-19 data, and testing on Monkeypox data, also demonstrates strong performance with an 89.25% accuracy and 94.16% F1-score, exceeding the performance of the same baseline models in the cross-domain setting. These results highlight the effectiveness of our approach for cross-domain misinformation detection and its potential for broader application in combating misinformation across diverse contexts.