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 ({\_}k{\_}=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.
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