Many web-based pharmaceutical e-commerce platforms allow consumers to post open-ended textual reviews based on their purchase experiences. Understanding the true voice of consumers by analyzing such a large amount of user-generated content is of great significance to pharmaceutical manufacturers and e-commerce websites. The aim of this paper is to automatically extract hidden topics from web-based drug reviews using the structural topic model (STM) to examine consumers’ concerns when they buy drugs online. The STM is a probabilistic extension of Latent Dirichlet Allocation (LDA), which allows the consolidation of document-level covariates. This innovation allows us to capture consumer dissatisfaction along with their dynamics over time. We extract 12 topics, and five of them are negative topics representing consumer dissatisfaction, whose appearances in the negative reviews are substantially higher than those in the positive reviews. We also come to the conclusion that the prevalence of these five negative topics has not decreased over time. Furthermore, our results reveal that the prevalence of price-related topics has decreased significantly in positive reviews, which indicates that low-price strategies are becoming less attractive to customers. To the best of our knowledge, our work is the first study using STM to analyze the unstructured textual data of drug reviews, which enhances the understanding of the aspects of drug consumer concerns and contributes to the research of pharmaceutical e-commerce literature.

The new coronavirus epidemic (COVID-19) has received widespread attention, causing the health crisis across the world. Massive information about the COVID-19 has emerged on social networks. However, not all information disseminated on social networks is true and reliable. In response to the COVID-19 pandemic, only real information is valuable to the authorities and the public. Therefore, it is an essential task to detect rumors of the COVID-19 on social networks. In this paper, we attempt to solve this problem by using an approach of machine learning on the platform of Weibo. First, we extract text characteristics, user-related features, interaction-based features, and emotion-based features from the spread messages of the COVID-19. Second, by combining these four types of features, we design an intelligent rumor detection model with the technique of ensemble learning. Finally, we conduct extensive experiments on the collected data from Weibo. Experimental results indicate that our model can significantly improve the accuracy of rumor detection, with an accuracy rate of 91% and an AUC value of 0.96.

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With the growth of mobile social networks (MSNs), crowdsourced information could be used for recommendation to mobile users. However, it is quite vulnerable to Sybil attacks, where attackers post fake information or reviews to mislead users for business benefits. To address this problem, existing detection models mainly use graph-based techniques or extract features of users. However, these approaches either rely on strong assumptions or lack generalization. Therefore, we propose a novel Sybil detection model based on generative adversarial networks (GANs), which contains a feature extractor, a domain classifier, and a Sybil detector. First, the feature extractor is proposed to identify the rich information in the review text with the neural network model of TextCNN. Second, the domain classifier is implemented by a neural network discriminator and is able to extract common features. Third, the Sybil detector is utilized to discriminate the fake review. Finally, the minimax game between the domain classifier and Sybil detector forms a GAN and enhances the overall generalization ability of the model. Extensive experiments show that our model has a high detection accuracy against Sybil attacks.