Detecting health misinformation in online health communities: Incorporating behavioral features into machine learning based approaches

TLDR: A novel health misinformation detection model was proposed which incorporated the central- level features and the peripheral-level features (including linguistic features, sentiment features, and user behavioral features) and correctly detected about 85% of the health misinformation.

Abstract: Curbing the diffusion of health misinformation on social media has long been a public concern since the spread of such misinformation can have adverse effects on public health. Previous studies mainly relied on linguistic features and textual features to detect online health-related misinformation. Based on the Elaboration Likelihood Model (ELM), this study proposed that the features of online health misinformation can be classified into two levels: central-level and peripheral-level. In this study, a novel health misinformation detection model was proposed which incorporated the central-level features (including topic features) and the peripheral-level features (including linguistic features, sentiment features, and user behavioral features). In addition, the following behavioral features were introduced to reflect the interaction characteristics of users: Discussion initiation, Interaction engagement, Influential scope, Relational mediation, and Informational independence. Due to the lack of a labeled dataset, we collected the dataset from a real online health community in order to provide a real scenario for data analysis. Four types of misinformation were identified through the coding analysis. The proposed model and its individual features were validated on the real-world dataset. The model correctly detected about 85% of the health misinformation. The results also suggested that behavioral features were more informative than linguistic features in detecting misinformation. The findings not only demonstrated the efficacy of behavioral features in health misinformation detection but also offered both methodological and theoretical contributions to misinformation detection from the perspective of integrating the features of messages as well as the features of message creators.