An attention-based deep learning model for credibility assessment of online health information

With the surge of searching and reading online health-based articles, maintaining the quality and credibility of online health-based articles has become crucial. The circulation of deceptive health information on numerous social media sites can mislead people and can potentially cause adverse effects on people's health. To address these problems, this work uses deep learning approaches to automate the assessment and scoring of online health-related articles' credibility. The paper proposed an Attention-based Recurrent Multichannel Convolutional Neural Network (ARMCNN) model. The proposed model incorporates a BiLSTM layer, a multichannel CNN layer, and an attention layer and predicts the credibility of online health information. To perform a reliable evaluation of the presented model, we utilize the health articles reviewed by the experts, compiled in a labeled dataset termed “Pubhealth,” which consists of thousands of health articles. The results are evaluated using five performance measures, accuracy, precision, recall, f1-score, and area under the ROC curve (AUC). Furthermore, we extensively compared the proposed model with different deep learning and machine learning models such as Long short-term memory (LSTM), Bidirectional LSTM, CNN (Convolutional neural network), and RNN-CNN. The experimental results showed that the proposed model produced state-of-the-art performance on the used dataset by achieving an accuracy of 0.88, precision of 0.92, recall of 0.87, f1-score of 0.90, and AUC of 0.94. Further, the proposed model yielded better performance than other benchmarked techniques for the credibility assessment of online health articles.