Injury prevention for older adults: A dataset of safety concern narratives from online reviews of mobility-related products

Data in Brief

Richard Gruss¹  Janay White²  Laura Prieto²  Alan Abrahams²  Namrata Mali³  Laura P. Sands⁴  Felipe Restrepo⁵  Johnathon P. Ehsani⁶  Peter Ractham⁷  Nohel Zaman⁸  David M Goldberg⁹ 

[1] Corresponding author.;Department of Business Information Technology, Virginia Tech, Pamplin Hall, 880W Campus Dr Suite 1007, Blacksburg, VA 24061, USA;Department of Computer Science, Virginia Tech, 2202 Kraft Dr SW, Blacksburg, VA 24060, USA;Department of Human Development and Family Science, Virginia Tech, Wallace Hall, 366, 295W Campus Dr 0416, Blacksburg, VA 24061, USA;Department of Industrial and Systems Engineering, Virginia Tech, 250 Durham Hall (MC 0118) 1145 Perry Street, Blacksburg, VA 24061, USA;Department of Information Systems and Business Analytics, Loyola Marymount University, 1 Loyola Marymount University Dr, Los Angeles, CA 90045, USA;Department of Management Information Systems, Thammasat University, 2 Phra Chan Alley, Khwaeng Phra Borom Maha Ratchawang, Khet Phra Nakhon, Bangkok 10200, Thailand;Department of Management, Radford University, P.O. Box 6954, Radford, VA 24142, USA;Management Information Systems Department, San Diego State University, 5500 Campanile Dr, San Diego, CA 92182, USA;

关键词: Safety concerns;    Health informatics;    Older adults;    Injury preventions;   

DOI  :  

来源: DOAJ

【 摘 要 】

Older adults are among the fastest-growing demographic groups in the United States, increasing by over a third this past decade. Consequently, the older adult consumer product market has quickly become a multi-billion-dollar industry in which millions of products are sold every year. However, the rapidly growing market raises the potential for an increasing number of product safety concerns and consumer product-related injuries among older adults. Recent manufacturer and consumer injury prevention efforts have begun to turn towards online reviews, as these provide valuable information from which actionable, timely intelligence can be derived and used to detect safety concerns and prevent injury. The presented dataset contains 1966 curated online product reviews from consumers, equally distributed between safety concerns and non-concerns, pertaining to product categories typically intended for older adults. Identified safety concerns were manually sub-coded across thirteen dimensions designed to capture relevant aspects of the consumer's experience with the purchased product, facilitate the safety concern identification and sub-classification process, and serve as a gold-standard, balanced dataset for text classifier learning.

【 授权许可】

Unknown