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.

中文翻译：

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基于注意力的深度学习模型用于在线健康信息的可信度评估

随着在线健康文章搜索和阅读的激增，维护在线健康文章的质量和可信度变得至关重要。许多社交媒体网站上传播欺骗性健康信息可能会误导人们，并可能对人们的健康造成不利影响。为了解决这些问题，这项工作使用深度学习方法来自动评估和评分在线健康相关文章的可信度。论文提出了一种基于注意力的循环多通道卷积神经网络（ARMCNN）模型。所提出的模型结合了 BiLSTM 层、多通道 CNN 层和注意力层，并预测在线健康信息的可信度。为了对所提出的模型进行可靠的评估，我们利用专家审查的健康文章，这些文章编译在名为“Pubhealth”的标记数据集中，该数据集由数千篇健康文章组成。结果使用五个性能指标进行评估：准确度、精确度、召回率、f1 分数和 ROC 曲线下面积 (AUC)。此外，我们将所提出的模型与不同的深度学习和机器学习模型（例如长短期记忆（LSTM）、双向 LSTM、CNN（卷积神经网络）和 RNN-CNN）进行了广泛比较。实验结果表明，所提出的模型在所使用的数据集上产生了最先进的性能，准确度为 0.88，精度为 0.92，召回率为 0.87，f1 得分为 0.90，AUC 为 0.94。此外，所提出的模型在在线健康文章的可信度评估方面比其他基准技术具有更好的性能。