Sentiment analysis of tweets on alopecia areata, hidradenitis suppurativa, and psoriasis: Revealing the patient experience

Irene Tai-Lin Lee; Sin-Ei Juang; Steven T Chen; Christine Ko; Kevin Sheng-Kai Ma

doi:10.3389/fmed.2022.996378

Sentiment analysis of tweets on alopecia areata, hidradenitis suppurativa, and psoriasis: Revealing the patient experience

Front Med (Lausanne). 2022 Oct 31:9:996378. doi: 10.3389/fmed.2022.996378. eCollection 2022.

Authors

Irene Tai-Lin Lee¹, Sin-Ei Juang², Steven T Chen³, Christine Ko^{4

5}, Kevin Sheng-Kai Ma^{3

6

7

8}

Affiliations

¹ Department of Radiology, Far Eastern Memorial Hospital, New Taipei City, Taiwan.
² Department of Anesthesiology, College of Medicine, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University, Kaohsiung, Taiwan.
³ Department of Dermatology, Harvard Medical School, Massachusetts General Hospital, Boston, MA, United States.
⁴ Department of Dermatology, Yale University, New Haven, CT, United States.
⁵ Department of Pathology, Yale University, New Haven, CT, United States.
⁶ Center for Global Health, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States.
⁷ Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, United States.
⁸ College of Electrical Engineering and Computer Science, Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan.

Abstract

Background: Chronic dermatologic disorders can cause significant emotional distress. 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). Although all skin conditions were associated with a negative polarity, their average was significantly less negative than FM (p < 0.0001). Tweets from private accounts were more negative, especially for AA (p = 0.0082). Words reflecting patients' psychological states varied in different diseases. "Anxiety" was observed in posts on AA and FM but not posts on HS and PsO, while "crying" was frequently used in posts on HS. 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).

Conclusion: The use of Twitter sentiment analysis is a promising method to document patients' experience of skin diseases, which may improve patient care through bridging misconceptions and knowledge gaps between patients and healthcare professionals.

Keywords: Twitter; alopecia areata; hidradenitis suppurativa; mental health; psoriasis; sentiment analysis.