Please use this identifier to cite or link to this item: https://doi.org/10.2196/31366
Title: Contemporary english pain descriptors as detected on social media using artificial intelligence and emotion analytics algorithms: Cross-sectional study
Authors: Tan, MY 
Goh, CE 
Tan, HH 
Keywords: McGill Pain Questionnaire
artificial intelligence
emotion analytics
pain descriptors
social media
Issue Date: 1-Nov-2021
Publisher: JMIR Publications Inc.
Citation: Tan, MY, Goh, CE, Tan, HH (2021-11-01). Contemporary english pain descriptors as detected on social media using artificial intelligence and emotion analytics algorithms: Cross-sectional study. JMIR Formative Research 5 (11) : e31366-. ScholarBank@NUS Repository. https://doi.org/10.2196/31366
Abstract: Background: Pain description is fundamental to health care. The McGill Pain Questionnaire (MPQ) has been validated as a tool for the multidimensional measurement of pain; however, its use relies heavily on language proficiency. Although the MPQ has remained unchanged since its inception, the English language has evolved significantly since then. The advent of the internet and social media has allowed for the generation of a staggering amount of publicly available data, allowing linguistic analysis at a scale never seen before. Objective: The aim of this study is to use social media data to examine the relevance of pain descriptors from the existing MPQ, identify novel contemporary English descriptors for pain among users of social media, and suggest a modification for a new MPQ for future validation and testing. Methods: All posts from social media platforms from January 1, 2019, to December 31, 2019, were extracted. Artificial intelligence and emotion analytics algorithms (Crystalace and CrystalFeel) were used to measure the emotional properties of the text, including sarcasm, anger, fear, sadness, joy, and valence. Word2Vec was used to identify new pain descriptors associated with the original descriptors from the MPQ. Analysis of count and pain intensity formed the basis for proposing new pain descriptors and determining the order of pain descriptors within each subclass. Results: A total of 118 new associated words were found via Word2Vec. Of these 118 words, 49 (41.5%) words had a count of at least 110, which corresponded to the count of the bottom 10% (8/78) of the original MPQ pain descriptors. The count and intensity of pain descriptors were used to formulate the inclusion criteria for a new pain questionnaire. For the suggested new pain questionnaire, 11 existing pain descriptors were removed, 13 new descriptors were added to existing subclasses, and a new Psychological subclass comprising 9 descriptors was added. Conclusions: This study presents a novel methodology using social media data to identify new pain descriptors and can be repeated at regular intervals to ensure the relevance of pain questionnaires. The original MPQ contains several potentially outdated pain descriptors and is inadequate for reporting the psychological aspects of pain. Further research is needed to examine the reliability and validity of the revised MPQ.
Source Title: JMIR Formative Research
URI: https://scholarbank.nus.edu.sg/handle/10635/230977
ISSN: 2561326X
2561326X
DOI: 10.2196/31366
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