TY - JOUR
T1 - The Long COVID experience from a patient's perspective
T2 - a clustering analysis of 27,216 Reddit posts
AU - Ayadi, Hanin
AU - Bour, Charline
AU - Fischer, Aurélie
AU - Ghoniem, Mohammad
AU - Fagherazzi, Guy
N1 - Funding Information:
The authors would like to thank the Luxembourg National Research Fund (FNR) and the Luxembourg Institute of Health (LIH) for their support.
Funding Information:
This work was supported by the Luxembourg National Research Fund (FNR) (DOVA-LUX project, grant number 16487884) and the Luxembourg Institute of Health (LIH).
Publisher Copyright:
Copyright © 2023 Ayadi, Bour, Fischer, Ghoniem and Fagherazzi.
PY - 2023/8/17
Y1 - 2023/8/17
N2 - Objective: This work aims to study the profiles of Long COVID from the perspective of the patients spontaneously sharing their experiences and symptoms on Reddit. Methods: We collected 27,216 posts shared between July 2020 and July 2022 on Long COVID-related Reddit forums. Natural language processing, clustering techniques and a Long COVID symptoms lexicon were used to extract the different symptoms and categories of symptoms and to study the co-occurrences and correlation between them. Results: More than 78% of the posts mentioned at least one Long COVID symptom. Fatigue (29.4%), pain (22%), clouded consciousness (19.1%), anxiety (17.7%) and headaches (15.6%) were the most prevalent symptoms. They also highly co-occurred with a variety of other symptoms (e.g., fever, sinonasal congestion). Different categories of symptoms were found: general (45.5%), neurological/ocular (42.9%), mental health/psychological/behavioral (35.2%), body pain/mobility (35.1%) and cardiorespiratory (31.2%). Posts focusing on other concerns of the community such as vaccine, recovery and relapse and, symptom triggers were detected. Conclusions: We demonstrated the benefits of leveraging large volumes of data from Reddit to characterize the heterogeneity of Long COVID profiles. General symptoms, particularly fatigue, have been reported to be the most prevalent and frequently co-occurred with other symptoms. Other concerns, such as vaccination and relapse following recovery, were also addressed by the Long COVID community.
AB - Objective: This work aims to study the profiles of Long COVID from the perspective of the patients spontaneously sharing their experiences and symptoms on Reddit. Methods: We collected 27,216 posts shared between July 2020 and July 2022 on Long COVID-related Reddit forums. Natural language processing, clustering techniques and a Long COVID symptoms lexicon were used to extract the different symptoms and categories of symptoms and to study the co-occurrences and correlation between them. Results: More than 78% of the posts mentioned at least one Long COVID symptom. Fatigue (29.4%), pain (22%), clouded consciousness (19.1%), anxiety (17.7%) and headaches (15.6%) were the most prevalent symptoms. They also highly co-occurred with a variety of other symptoms (e.g., fever, sinonasal congestion). Different categories of symptoms were found: general (45.5%), neurological/ocular (42.9%), mental health/psychological/behavioral (35.2%), body pain/mobility (35.1%) and cardiorespiratory (31.2%). Posts focusing on other concerns of the community such as vaccine, recovery and relapse and, symptom triggers were detected. Conclusions: We demonstrated the benefits of leveraging large volumes of data from Reddit to characterize the heterogeneity of Long COVID profiles. General symptoms, particularly fatigue, have been reported to be the most prevalent and frequently co-occurred with other symptoms. Other concerns, such as vaccination and relapse following recovery, were also addressed by the Long COVID community.
KW - artificial intelligence
KW - digital health
KW - Long COVID
KW - machine learning
KW - natural language processing
KW - patient-reported outcomes
KW - public health
KW - social media
UR - http://www.scopus.com/inward/record.url?scp=85169611450&partnerID=8YFLogxK
UR - https://pubmed.ncbi.nlm.nih.gov/37663849
U2 - 10.3389/fpubh.2023.1227807
DO - 10.3389/fpubh.2023.1227807
M3 - Article
C2 - 37663849
SN - 2296-2565
VL - 11
JO - Frontiers in Public Health
JF - Frontiers in Public Health
M1 - 1227807
ER -