Abstract
Past years have seen rising engagement among caregivers in online health communities. Although studies indicate that this caregiver-generated online health information benefits patients, how such information can be perceived easily and correctly remains unclear. This study aims to fill this gap by exploring mechanisms to improve the perceived helpfulness of online health information. We propose a multi-method framework, including a novel Medical-Enriched DEep Learning (MEDEL) feature extraction method, econometric analyses, and a randomized experiment. The results show that when the medical language of health information is informal, the senior care information is more helpful. Our findings provide a theoretical foundation to understand the influence of language formality on many other business communications. Our proposed multi-method approach can also be generalized to investigate research questions involving complex textual features. Forum sites could leverage our proposed approach to improve the helpfulness of online health information and user satisfaction.
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