\m: COVID-19 Healthcare Misinformation Dataset
Table 4: Misinformation detection performance
['[EMPTY]', 'PR AUC', 'Precision', 'Recall', 'F1']
We implement all models with Keras. We randomly use the labels of 75% news articles for training and predict the remaining 25%. We set the hidden dimension of all models to 100. The word embeddings are initialized by GloVe [pennington2014glove] and the dimension of pre-trained word embeddings is set to 100. For deep learning methods, we use Adam with a minibatch of 50 articles on the dataset, and the training epoch is set as 10. For a fair comparison, we use the cross-entropy loss for all methods. However, as the dataset is quite imbalanced, the models tend to generate many fake positive cases. Thus, the recall and F1 values are far from being satisfactory. As the proportion of true and fake information is likely to be even more skewer, practical detection solutions need to handle this type of imbalance setting more.