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Sentiment analysis of tweets on alopecia areata, hidradenitis suppurativa, and psoriasis: Revealing the patient experience
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 (...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9660311/ https://www.ncbi.nlm.nih.gov/pubmed/36388938 http://dx.doi.org/10.3389/fmed.2022.996378 |
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author | Lee, Irene Tai-Lin Juang, Sin-Ei Chen, Steven T. Ko, Christine Ma, Kevin Sheng-Kai |
author_facet | Lee, Irene Tai-Lin Juang, Sin-Ei Chen, Steven T. Ko, Christine Ma, Kevin Sheng-Kai |
author_sort | Lee, Irene Tai-Lin |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-9660311 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-96603112022-11-15 Sentiment analysis of tweets on alopecia areata, hidradenitis suppurativa, and psoriasis: Revealing the patient experience Lee, Irene Tai-Lin Juang, Sin-Ei Chen, Steven T. Ko, Christine Ma, Kevin Sheng-Kai Front Med (Lausanne) Medicine 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. Frontiers Media S.A. 2022-10-31 /pmc/articles/PMC9660311/ /pubmed/36388938 http://dx.doi.org/10.3389/fmed.2022.996378 Text en Copyright © 2022 Lee, Juang, Chen, Ko and Ma. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Medicine Lee, Irene Tai-Lin Juang, Sin-Ei Chen, Steven T. Ko, Christine Ma, Kevin Sheng-Kai Sentiment analysis of tweets on alopecia areata, hidradenitis suppurativa, and psoriasis: Revealing the patient experience |
title | Sentiment analysis of tweets on alopecia areata, hidradenitis suppurativa, and psoriasis: Revealing the patient experience |
title_full | Sentiment analysis of tweets on alopecia areata, hidradenitis suppurativa, and psoriasis: Revealing the patient experience |
title_fullStr | Sentiment analysis of tweets on alopecia areata, hidradenitis suppurativa, and psoriasis: Revealing the patient experience |
title_full_unstemmed | Sentiment analysis of tweets on alopecia areata, hidradenitis suppurativa, and psoriasis: Revealing the patient experience |
title_short | Sentiment analysis of tweets on alopecia areata, hidradenitis suppurativa, and psoriasis: Revealing the patient experience |
title_sort | sentiment analysis of tweets on alopecia areata, hidradenitis suppurativa, and psoriasis: revealing the patient experience |
topic | Medicine |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9660311/ https://www.ncbi.nlm.nih.gov/pubmed/36388938 http://dx.doi.org/10.3389/fmed.2022.996378 |
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