Identifying Medical Self-Disclosure in Online Communities

Self-disclosure in online health conversations may offer a host of benefits, including earlier detection and treatment of medical issues that may have otherwise gone unaddressed. However, research analyzing medical self-disclosure in online communities is limited. We address this shortcoming by introducing a new dataset of health-related posts collected from online social platforms, categorized into three groups (No SELF-DISCLOSURE, POSSIBLE SELF-DISCLOSURE, and CLEAR SELF-DISCLOSURE) with high inter-annotator agreement (kappa = 0.88). We make this data available to the research community. We also release a predictive model trained on this dataset that achieves an accuracy of 81.02%, establishing a strong performance benchmark for this task.