... learning BILSTM-CNN classification model to identify user information processing characteristics, and then classify users into three groups. The LDA topic model is employed to analyze the information needs of these groups. Findings This research utilizes a BILSTM-CNN combined deep learning model...

... misinformation data. The ten most frequently used datasets are identified. Most studies (96.1%) developed or applied machine learning, especially deep learning models. The most advanced current misinformation detection models could achieve pretty high performance. For example, among 104 studies reporting...

... in a message thread were studied. Deep learning techniques, knowledge graphs (KG), and content analyses were used to perform aspect-based sentiment analysis (ABSA) of the messages and measure information distortion. Findings Debunking messages with higher source credibility (e.g. providing evidence from...

... in agriculture. The search query employed “Artificial Intelligence”, “Cognitive Computing”, “Expert System”, “Machine Learning”, “Deep learning”, “Neural Network”, “Decision Support System”, “Learning Algorithm”, “Learning Systems”, “Algorithmic”, “Supervised learning”, “Unsupervised learning”, “Reinforcement...

... but to automatically detect them remains a challenge. Design/methodology/approach We deployed the emerging deep learning approaches. Precisely, we used word embedding to encode semantic information of words occurring in the common microtext of social media and used variational autoencoder (VAE) to approximate...