Previously submitted to: JMIR Dermatology (no longer under consideration since Nov 23, 2022)
Date Submitted: Jun 29, 2022
Open Peer Review Period: Jun 29, 2022 - Aug 24, 2022
(closed for review but you can still tweet)
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Sentiment Analysis on Tweets Related to Alopecia Areata, Hidradenitis Suppurativa, and Psoriasis: Revealing the Patient Experience of Disease
ABSTRACT
Background:
Psychiatric comorbidities can commonly occur in patients suffering from chronic dermatologic disorders. Sentiment analysis of disease-related tweets helps identify patients’ experiences of skin disease.
Objective:
To analyze the expressed sentiments in tweets related to alopecia areata (AA), hidradenitis suppurativa (HS), and psoriasis (PsO) in comparison to fibromyalgia (FM).
Methods:
This is a cross-sectional analysis of Twitter users’ expressed sentiment on AA, HS, PsO, and FM. Tweets related to the diseases of interest were identified with keywords and hashtags for one month (April 2022) using the Twitter standard application programming interface (API). Text, account types, and numbers of retweets and likes were collected. The sentiment analysis was performed by the R ‘tidytext’ package using the AFINN lexicon.
Results:
A total of 1,505 tweets were randomly extracted, of which 243 (16.15%) referred to AA, 186 (12.36%) to HS, 510 (33.89%) to PsO, and 566 (37.61%) to FM. The mean sentiment score was -0.239 ± 2.90. AA, HS, and PsO had similar sentiment scores (p = 0.482) and the average was significantly more positive than FM (p < 0.0001). Tweets from private accounts were more negative, especially for AA (p = 0.0082). Words reflecting patients’ psychological states were found differently in different diseases. ‘Anxiety’ was associated with AA and FM posts but not with HS and PsO; while ‘crying’ was frequently used in HS posts. There was no definite correlation between the sentiment score and the number of retweets or likes, although negative AA tweets from public accounts received more retweets (p = 0.03511) and likes (p = 0.0228).
Conclusions:
The use of Twitter sentiment analysis is a promising method to document patients’ experience of skin disease. This technique has the potential to improve patient care, strategize public health policies, and direct organizational campaigns. Clinical Trial: Not applicable.
Citation
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