RHMD: A Real-World Dataset for Health Mention Classification on Reddit

Usman Naseem, Matloob Khushi, Jinman Kim, Adam G. Dunn

Abstract: People on social media share their thoughts and experiences using diseases and symptoms words other than to mention their health, which can introduce biases in data-driven public health applications. For the advancement of HMC research, in this study, we present a Reddit health mention dataset (RHMD), a new dataset of multi-domain Reddit data for the HMC. RHMD is composed of 10015 manually annotated Reddit posts that include 15 common disease or symptom terms and are labeled with four labels: personal health mentions (HMs), nonpersonal HMs, figurative HMs, and hyperbolic HMs. Empirical evaluation using recently proposed methods demonstrates the challenge of labeling user-generated text across these four types. Contributions to this work include the public release of a robustly annotated Reddit dataset (RHMD) for HM tasks and a comprehensive performance analysis of baseline methods. We expect the release of the dataset, and the evaluations will help facilitate the development of new methods for detecting HMs in the user-generated text. The dataset is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/usmaann/RHMD-Health-Mention-Dataset</uri> .