RESUMEN
BACKGROUND: Resources such as Google Trends and Reddit provide opportunities to gauge real-time popular interest in public health issues. Despite the potential for these publicly available and free resources to help optimize public health campaigns, use for this purpose has been limited. OBJECTIVE: The purpose of this study is to determine whether early public awareness of COVID-19 correlated with elevated public interest in other infectious diseases of public health importance. METHODS: Google Trends search data and Reddit comment data were analyzed from 2018 through 2020 for the frequency of keywords "chikungunya," "Ebola," "H1N1," "MERS," "SARS," and "Zika," 6 highly publicized epidemic diseases in recent decades. After collecting Google Trends relative popularity scores for each of these 6 terms, unpaired 2-tailed t tests were used to compare the 2020 weekly scores for each term to their average level over the 3-year study period. The number of Reddit comments per month with each of these 6 terms was collected and then adjusted for the total estimated Reddit monthly comment volume to derive a measure of relative use, analogous to the Google Trends popularity score. The relative monthly incidence of comments with each search term was then compared to the corresponding search term's pre-COVID monthly comment data, again using unpaired 2-tailed t tests. P value cutoffs for statistical significance were determined a priori with a Bonferroni correction. RESULTS: Google Trends and Reddit data both demonstrate large and statistically significant increases in the usage of each evaluated disease term through at least the initial months of the pandemic. Google searches and Reddit comments that included any of the evaluated infectious disease search terms rose significantly in the first months of 2020 above their baseline usage, peaking in March 2020. Google searches for "SARS" and "MERS" remained elevated for the entirety of the 2020 calendar year, as did Reddit comments with the words "Ebola," "H1N1," "MERS," and "SARS" (P<.001, for each weekly or monthly comparison, respectively). CONCLUSIONS: Google Trends and Reddit can readily be used to evaluate real-time general interest levels in public health-related topics, providing a tool to better time and direct public health initiatives that require a receptive target audience. The start of the COVID-19 pandemic correlated with increased public interest in other epidemic infectious diseases. We have demonstrated that for 6 distinct infectious causes of epidemics over the last 2 decades, public interest rose substantially and rapidly with the outbreak of COVID-19. Our data suggests that for at least several months after the initial outbreak, the public may have been particularly receptive to dialogue on these topics. Public health officials should consider using Google Trends and social media data to identify patterns of engagement with public health topics in real time and to optimize the timing of public health campaigns.
RESUMEN
Social media tools are widely used by dermatologic patients. Eczema and psoriasis, two of the most common inflammatory skin diseases, are well-represented on the social media site Reddit. We used natural language processing tools to examine comments in subreddits r/psoriasis and r/eczema (combined user base >187,000), tracking commenters' interest levels and sentiments related to common treatments for psoriasis and eczema as well as discussions of adverse drug reactions. All comments from 2014-2020 from the subreddits r/eczema (n = 196,571) and r/psoriasis (n = 123,144) were retrieved and processed using natural language processing tools. Comment volume in r/eczema related to antibacterial therapies, lifestyle changes, and prednisone decreased from 2014-2020, whereas phototherapy comments remained stable, and dupilumab comment volume increased. Comment volume in r/psoriasis for newer therapeutics (including biologics and apremilast) increased after Food and Drug Administration approval, whereas older therapies such as etanercept, adalimumab, and methotrexate decreased over time. Sentiment scores tended to decrease in the years after Food and Drug Administration approval. Among psoriasis treatments, calcipotriene and branded calcipotriene/betamethasone foam had the highest sentiment, whereas apremilast had the lowest overall sentiment score. These analyses also identified changes in patient interest levels and sentiment related to eczema and psoriasis treatments, suggesting an area for additional research.
RESUMEN
BACKGROUND: Reddit is a popular social media discussion forum. Reddit data can be analyzed with natural language processing techniques to gain insights into public health questions by tracking frequency of discussion on relevant topics over time and analysis of discussion content. OBJECTIVES: To apply natural language processing techniques to categorize, track, and gain insights from comments regarding skincare-related topics on Reddit using sentiment analysis and word search techniques. MATERIAL AND METHODS: Historical Reddit comments available on Google BigQuery from the r/SkincareAddiction subreddit were selected and preprocessed. Latent Dirichlet Allocation was applied to create topics. Selected topics were further investigated for interest over time, by determining comment frequencies of words of interest. Sentiment analysis was also applied to each topic. RESULTS: >3,000,000 comments were analyzed and classified into 25 topics. Topics related to sunscreen, diet, and exfoliants were examined for response frequencies over time, demonstrating seasonal variation. Taking comment frequencies demonstrated peaks containing "coral" and "oxybenzone" that corresponded to media coverage of sunscreen-associated coral bleaching. Queries containing "physical" and "mineral" demonstrated an evolution in word choice describing physical/mineral sunscreens over time. Sentiment analysis demonstrated a range from mildly positive to moderately positive sentiment across the five examined skincare topics. LIMITATIONS: Our analysis was limited to one subreddit category. Additionally, Latent Dirichlet Allocation is an unsupervised model; its accuracy cannot be readily assessed. Taking comment frequencies for words, while powerful, cannot be used to find word trends that are not intentionally queried by the user. CONCLUSIONS: Natural language processing is a powerful tool to examine large dermatology discussion forums and gain insights into patient perceptions of the field.