Naseem, Usman, Khushi, Matloob, Kim, Jinman, and Dunn, Adam G. (2022) RHMD: A Real-World Dataset for Health Mention Classification on Reddit. IEEE Transactions on Computational Social Systems. (In Press)

PDF (Published Version) - Published Version Restricted to Repository staff only

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 10 015 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.

Item ID:	79227
Item Type:	Article (Research - C1)
ISSN:	2329-924X
Copyright Information:	© 2022 IEEE.
Date Deposited:	03 Aug 2023 03:00
FoR Codes:	46 INFORMATION AND COMPUTING SCIENCES > 4602 Artificial intelligence > 460208 Natural language processing @ 100%
SEO Codes:	22 INFORMATION AND COMMUNICATION SERVICES > 2204 Information systems, technologies and services > 220403 Artificial intelligence @ 100%
	More Statistics