Topical steroid withdrawal (TSW) is a term used to describe a set of adverse effects following cessation of prolonged and/or higher-potency topical corticosteroid use. Evidence regarding the existence of TSW as a distinct diagnosis is insufficient and contradictory, creating controversy within the medical community.¹ In a study of dermatologists in the United Kingdom, the majority of respondents believed that “most people complaining of TSW are simply experiencing ordinary eczema which relapsed because the TCS was stopped.”² Unsurprisingly, patients may feel dismissed by providers who do not acknowledge TSW, resulting in avoidance of traditional medical treatments and topical corticosteroids in favor of alternative treatments.^1,2

The phenomenon of TSW has garnered increasing attention on social media. In fact, the aforementioned study of UK dermatologists found that some participants considered TSW a purely social construct due to its online presence.² Compared to 2016, the year 2020 saw a 592% increase in performance of the hashtag #topicalsteroidwithdrawal on several social media platforms, including Reddit.³ A study of Reddit users suggests that those in search of health information on Reddit are likely to act on information provided on the platform, regardless of credibility.⁴ Therefore, Reddit may serve as a key platform to examine the perspectives and actions of those concerned with TSW.

The ill-defined and debated nature of TSW warrants analysis of perspectives in the subreddit “r/TS_Withdrawal.” Understanding how individuals in “r/TS_Withdrawal” perceive dermatologic care is essential to collaborating with patients who believe they are experiencing TSW. There also exist subreddits for conditions that are more understood and uniformly managed by dermatologists, such as “r/Psoriasis.” This study seeks to analyze and compare the sentiments of Reddit users toward dermatologists in the “r/TS_Withdrawal” and “r/Psoriasis” subreddits. We hypothesize that sentiment towards dermatologists in “r/TS_Withdrawal” will be more negative than those in “r/Psoriasis” due to the ambiguity within the field regarding TSW, with much less uniformity in care, compared to psoriasis.

The 999 “top” Reddit posts were downloaded from the “r/TS_Withdrawal” and “r/Psoriasis” subreddits using the Python Reddit API Wrapper. While 1000 posts were queried, 999 were extracted because one post in each subreddit was not retrievable.

Posts with no body text (ie if only a picture or title was posted), were removed from the analysis. The remaining posts were tokenized into paragraphs. Previous analyses of lexicon-based sentiment analysis tools, including the one employed in this study, demonstrate better performance with shorter microblog-like input.^5,6

The presence of the terms “dermatologist(s),” “derm(s),” “doctor(s),” “dermatology,” and “physician(s)” was used to separate “dermatologist-related paragraphs” (DRPs), containing sentiment towards dermatologists, from “non-dermatologist-related paragraphs” (non-DRPs). One researcher (CC) validated the DRPs using the following inclusion criteria, moving paragraphs to the non-DRP group when appropriate. The paragraph had to contain at least one of the following themes related to dermatologists: an opinion, feeling, evaluation of dermatologist knowledge, treatment, consulting a dermatologist, perceived motives, and collaboration. Paragraphs mentioning doctors that were explicitly not dermatologists were excluded. Additional details can be found in Table 1.

241748 Table 1.

Sentiment analysis was performed using the Sentiment Intensity Analyser of the Python Natural Language Toolkit (https://www.nltk.org/). This tool uses a pretrained sentiment analyzer called VADER (Valence Aware Dictionary and sEntiment Reasoner). VADER’s rule-based model considers the sentiment of the words, as well as features such as capitalization, punctuation, and negation.⁵ The outputted sentiment score (SS) incorporates both polarity (positivity/negativity) and intensity of sentiment, ranging from –1 (most negative) to +1 (most positive). SSs were calculated for DRPs and non-DRPs. Using a Fisher’s exact test and Student’s t-test, SSs of DRPs and non-DRPs were compared within each subreddit. SSs of DRPs were compared between the two subreddits using a Student’s t-test.

Of the top 999 posts in “r/TS_Withdrawal,” 250 posts had no text and were excluded. The remaining 749 posts (3,690 paragraphs) ranged from November 22, 2015 to January 16, 2024. 291 paragraphs mentioned dermatologist-related terms. After validation, 72 paragraphs were removed, leaving 219 DRPs. Of the top 999 posts in “r/Psoriasis,” 691 posts had no text and were excluded. The remaining 308 posts (1,493 paragraphs) ranged from September 15, 2014 to January 17, 2024. 140 paragraphs mentioned dermatologist-related terms. After validation, 54 paragraphs were removed, leaving 86 DRPs. Representative quotes are presented in Table 2.

241749 Table 2.

Having a relatively negative sentiment was defined as having a SS in the lowest quartile among all 5,183 paragraphs.⁷ Within each subreddit, DRPs were significantly more likely to have negative sentiment than non-DRPs (p < 0.001) with odds ratios of 2.68 (95% CI = 2.02 - 3.57) and 2.60 (95% CI = 1.59 - 4.18) in “r/TS_Withdrawal” and “r/Psoriasis,” respectively.

Within “r/TS_Withdrawal,” DRPs had a mean SS of -0.14, which was significantly lower (p < 0.001) than non-DRPs (mean SS = 0.069). Within “r/Psoriasis,” DRPs had a mean SS of -0.055, which was significantly lower (p = 0.008) than non-DRPs (mean SS = 0.12). The mean SS of DRPs in “r/Psoriasis” was not significantly higher than that of DRPs in “r/TS_Withdrawal” (p = 0.30). Figure 1 displays the distribution of sentiment scores for DRPs and non-DRPs in each subreddit.

Figure 1.

This study examined sentiment towards dermatologists among Reddit users creating posts in the “r/TS_Withdrawal” and “r/Psoriasis” subreddits. In both subreddits, DRPs had significantly more negative sentiment than non-DRPs. This finding suggests that users in both subreddits possess negative sentiments towards dermatologists. While studies exist regarding patient satisfaction with psoriasis treatment, few studies have been conducted on perceptions of dermatologists held by psoriasis and TSW patients. Nonetheless, this finding is consistent with a qualitative study conducted on patients with acne, psoriasis, and eczema.⁸ Participants commonly reported negative experiences with general practitioners and dermatologists, citing insensitivity to the psychological implications of their conditions.⁸ A study of dermatology outpatients also found the main determinant in patient satisfaction to be the physician’s interpersonal skills.⁹ However, these findings contradict a study of public perception of dermatologists in Europe, which found high levels of satisfaction among participants who consulted a dermatologist within the last 3 years.¹⁰ To our knowledge, there are no studies examining the perspectives of those who may have TSW towards dermatologists. Further research is needed to characterize patient perceptions of dermatologists.

While we hypothesized that DRPs in “r/TS_Withdrawal” would have more negative sentiment than “r/Psoriasis,” the SSs of DRPs in the subreddits did not differ significantly. Therefore, sentiment towards dermatologists among those discussing a relatively well-managed condition, psoriasis, did not differ from those discussing TSW. Our findings suggest that patients experiencing either condition possess negative sentiments towards dermatologists, despite vast differences in existing knowledge between psoriasis and TSW. Regardless of the level of knowledge, it appears that patient perspectives, collaboration, and trust are necessary to maintain patient-physician relationships and improve management of dermatological conditions.

This study has several limitations. The sample size included only Reddit users who posted to “r/Psoriasis” or “r/TS_Withdrawal.” The perspectives of this population may not be representative of those who do not post or do not use Reddit. While our analysis would be strengthened with analysis of additional platforms, such as Facebook, Instagram, or Twitter/X, Reddit uniquely offered forums for both psoriasis and TSW. Facebook, while possessing groups and posts about these topics, have many scattered posts, many of which are too brief to analyze. Also, we were unable to find a tool to query only the top posts in the groups. The Python Reddit API wrapper and Reddit’s upvote function allowed us to focus on only the top posts that had the most support from other users. For these reasons, we limited our focus to the Reddit platform. Additionally, those who see a dermatologist for their condition likely have a more severe disease presentation and may reflect more negatively on their experience, especially if dermatologic care did not lead to as much improvement as anticipated. Those with more emotional experiences are more likely to post about their experience, influencing SSs. Since VADER works best on short texts, longer paragraphs may not have had representative SSs. Finally, inclusion of posts containing the term “doctor(s)” assumes that the “doctor” was a dermatologist, unless otherwise stated.

The findings of this Reddit sentiment analysis reveal significantly negative sentiment towards dermatologists in the r/TS_Withdrawal and r/Psoriasis subreddits. This negative sentiment highlights the need for the field to place greater emphasis on fostering positive patient experiences, feelings, and perceptions of dermatologic care.

This research received no funding.

Figure 1. Graphical representation of sentiment scores. Histograms representing the sentiment scores of dermatologist-related paragraphs and non-dermatologist-related paragraphs in “r/Psoriasis” and “r/TS_Withdrawal”. The vertical red line represents the mean sentiment score in each group of paragraphs. The slope of black line represents the degree and direction of the overall trend of sentiment scores in each group of paragraphs. The slope for each group was determined by creating a linear regression model with the center value of each histogram bin as the independent variable and the density of each bin as the dependent variable.