Mask-related acne in the COVID-19 pandemic: an analysis of Twitter posts and influencers.
Ontology highlight
ABSTRACT:
SUBMITTER: Drozdowski R
PROVIDER: S-EPMC8013391 | biostudies-literature | 2021 Jul
REPOSITORIES: biostudies-literature
Publications
Mask-related acne in the COVID-19 pandemic: an analysis of Twitter posts and influencers.
Drozdowski R R Gronbeck C C Feng H H
Clinical and experimental dermatology 20210427 5
Similar Datasets
Project description:IntroductionThe objective of this study was to analyze the messages of influential emergency medicine (EM) Twitter users in the United States (US) during the early stages of the coronavirus disease 2019 (COVID-19) global pandemic by characterizing the themes, emotional tones, temporal viewpoints, and depth of engagement with the tweets.MethodsWe performed a retrospective mixed-methods analysis of publicly available Twitter data derived from the publicly available "Coronavirus Tweet IDs" dataset, March 3, 2020-May 1, 2020. Original tweets and modified retweets in the dataset by 50 influential EM Twitter users in the US were analyzed using linguistic software to report the emotional tone and temporal viewpoint. We qualitatively analyzed a 25% random subsample and report themes.ResultsThere were 1315 tweets available in the dataset from 36/50 influential EM Twitter users in the US. The majority of tweets were either positive (455/1315, 34.6%) or neutral (407/1315, 31%) in tone and focused on the present (1009/1315, 76.7%). Qualitative analysis identified six distinct themes, with users most often sharing news or clinical information.ConclusionsDuring the early weeks of the COVID-19 pandemic, influential EM Twitter users in the US delivered mainly positive or neutral messages, most often pertaining to news stories or information directly relating to patient care. The majority of these messages led to engagement by other users. This study underscores how EM influencers can leverage social media in public health outbreaks to bring attention to topics of importance.
| S-EPMC8203008 | biostudies-literature
Project description:IntroductionTwitter data have been used to surveil public sentiment about tobacco products; however, most tobacco-related Twitter research has been conducted with English-language posts. There is a gap in the literature on tobacco-related discussions on Twitter in languages other than English. This study summarized tobacco-related discussions in Spanish on Twitter.MethodsA set of Spanish terms reflecting electronic cigarettes (eg, "cigarillos electrónicos"), cigarettes (eg, "pitillo"), and cigars (eg, "cigaro") were identified. A content analysis of tweets (n = 1352) drawn from 2021 was performed to examine themes and sentiment. An initial codebook was developed in English then translated to Spanish and then translated back to English by a bilingual (Spanish and English) member of the research team. Two bilingual members of the research team coded the tweets into themes and sentiment.ResultsThemes in the tweets included (1) product promotion (n = 168, 12.4%), (2) health warnings (n = 161, 11.9%), (3) tobacco use (n = 136, 10.1%), (4) health benefits of vaping (n = 58, 4.3%), (5) cannabis use (n = 50, 3.7%), (6) cessation (n = 47, 3.5%), (7) addiction (n = 33, 2.4%), (8) policy (n = 27, 2.0%), and (9) polysubstance use (n = 12, 0.9%). Neutral (n = 955, 70.6%) was the most common category of sentiment observed in the data.ConclusionsTobacco products are discussed in multiple languages on Twitter and can be summarized by bilingual research teams. Future research should determine if Spanish-speaking individuals are frequently exposed to pro-tobacco content on social media and if such exposure increases susceptibility to use tobacco among never users or sustained use among current users.ImplicationsSpanish-language pro-tobacco content exists on Twitter, which has implications for Spanish-speaking individuals who may be exposed to this content. Spanish-language pro-tobacco-related posts may help normalize tobacco use among Spanish-speaking populations. As a result, anti-tobacco tweets in Spanish may be necessary to counter areas of the online environment that can be considered pro-tobacco.
| S-EPMC11519019 | biostudies-literature
Project description:BackgroundAs vaping rapidly becomes more prevalent, social media data can be harnessed to capture individuals' discussions of e-cigarette products quickly. The JUUL vaporizer is the latest advancement in e-cigarette technology, which delivers nicotine to the user from a device that is the size and shape of a thumb drive. Despite JUUL's growing popularity, little research has been conducted on JUUL. Here we utilized Twitter data to determine the public's early experiences with JUUL describing topics of posts.MethodsTwitter posts containing the term "JUUL" were obtained for 1 April 2107 to 14 December 2017. Text classifiers were used to identify topics in posts (n = 81,689).ResultsThe most prevalent topic wasPerson Tagging (use of @username to tag someone in a post) at 20.48% followed by Pods (mentions of JUUL's refill cartridge) at 14.72% and Buying (mentions of purchases) at 10.49%. The topic School (posts indicative of using JUUL or seeing someone use JUUL while at elementary, middle, or high school) comprised 3.66% of posts. The topic of Quit Smoking was rare at 0.29%.ConclusionsData from social media may be used to extend the surveillance of newly introduced vaping products. Findings suggest Twitter users are bonding around, and inquiring about, JUUL on social media. JUUL's discreetness may facilitate its use in places where vaping is prohibited. Educators may be in need of training on how to identify JUUL in the classroom. Despite JUUL's branding as a smoking alternative, very few Twitter users mentioned smoking cessation with JUUL.
| S-EPMC6693487 | biostudies-literature
Project description:IntroductionE-liquid is the solution aerosolized by e-cigarette devices to produce vapor. Continuously evolving e-liquids, and corresponding devices, can affect user experiences associated with these products. Twitter conversations about e-liquids can capture salient behavioral, social, and communicative cues associated with e-liquids. We analyzed Twitter data to characterize key topics of conversation about e-liquids to inform surveillance, and regulatory efforts.MethodsTwitter posts containing e-liquid-related terms ("e-liquid(s)," "e-juice(s)") were obtained from 1 January 2018 to 31 December 2018. Text classifiers were used to identify topics of the posts (n = 15,927).ResultsThe most prevalent topic was Promotional at 29.35% followed by Flavors at 24.22%, and Person Tagging at 21.47%. Juice Composition was next most prevalent at 17.61% followed by Cannabis at 16.83%, and Nicotine Health Risks at 6.39%. Quit Smoking was rare at 0.57%.ConclusionThese results suggest that flavors, cannabis, health risks of nicotine, and composition warrant consideration as targets in future surveillance, public policy, and interventions addressing the use of e-liquids. Twitter provides ample opportunity to influence the normalization, and uptake, of e-cigarette-related products among non-smokers and youth, unless regulatory restrictions, and counter messaging campaigns are developed to reduce this risk.
| S-EPMC6579902 | biostudies-literature
Project description:Amidst the COVID-19 vaccination, Twitter is one of the most popular platforms for discussions about the COVID-19 vaccination. These types of discussions most times lead to a compromise of public confidence toward the vaccine. The text-based data generated by these discussions are used by researchers to extract topics and perform sentiment analysis at the provincial, country, or continent level without considering the local communities. The aim of this study is to use clustered geo-tagged Twitter posts to inform city-level variations in sentiments toward COVID-19 vaccine-related topics in the three largest South African cities (Cape Town, Durban, and Johannesburg). VADER, an NLP pre-trained model was used to label the Twitter posts according to their sentiments with their associated intensity scores. The outputs were validated using NB (0.68), LR (0.75), SVMs (0.70), DT (0.62), and KNN (0.56) machine learning classification algorithms. The number of new COVID-19 cases significantly positively correlated with the number of Tweets in South Africa (Corr = 0.462, P < 0.001). Out of the 10 topics identified from the tweets using the LDA model, two were about the COVID-19 vaccines: uptake and supply, respectively. The intensity of the sentiment score for the two topics was associated with the total number of vaccines administered in South Africa (P < 0.001). Discussions regarding the two topics showed higher intensity scores for the neutral sentiment class (P = 0.015) than for other sentiment classes. Additionally, the intensity of the discussions on the two topics was associated with the total number of vaccines administered, new cases, deaths, and recoveries across the three cities (P < 0.001). The sentiment score for the most discussed topic, vaccine uptake, differed across the three cities, with (P = 0.003), (P = 0.002), and (P < 0.001) for positive, negative, and neutral sentiments classes, respectively. The outcome of this research showed that clustered geo-tagged Twitter posts can be used to better analyse the dynamics in sentiments toward community-based infectious diseases-related discussions, such as COVID-19, Malaria, or Monkeypox. This can provide additional city-level information to health policy in planning and decision-making regarding vaccine hesitancy for future outbreaks.
| S-EPMC9412204 | biostudies-literature
Project description:BackgroundThe COVID-19 pandemic has drawn attention to various inequalities in global societies, highlighting discrepancies in terms of safety, accessibility, and overall health. In particular, sex workers are disproportionately at risk due to the nature of their work and the social stigma that comes alongside it.ObjectiveThis study examines how public social media can be used as a tool of professional and personal expression by sex workers during the COVID-19 pandemic. We aimed to explore an underresearched topic by focusing on sex workers' experiences with the ongoing COVID-19 pandemic on the social media platform Twitter. In particular, we aimed to find the main issues that sex workers discuss on social media in relation to the COVID-19 pandemic.MethodsA literature review followed by a qualitative analysis of 1458 (re)tweets from 22 sex worker Twitter accounts was used for this study. The tweets were qualitatively coded by theme through the use of intercoder reliability. Empirical, experimental, and observational studies were included in this review to provide context and support for our findings.ResultsIn total, 5 major categories were identified as a result of the content analysis used for this study: concerns (n=542, 37.2%), solicitation (n=336, 23.0%), herd mentality (n=231, 15.8%), humor (n=190, 13.0%), and blame (n=146, 10.0%). The concerns category was the most prominent category, which could be due to its multifaceted nature of including individual concerns, health issues, concerns for essential workers and businesses, as well as concerns about inequalities or intersectionality. When using gender as a control factor, the majority of the results were not noteworthy, save for the blame category, in which sexual and gender minorities (SGMs) were more likely to post content.ConclusionsThough there has been an increase in the literature related to the experiences of sex workers, this paper recommends that future studies could benefit from further examining these 5 major categories through mixed methods research. Examining this phenomenon could recognize the challenges unique to this working community during the COVID-19 pandemic and potentially reduce the widespread stigma associated with sex work in general.
| S-EPMC9285671 | biostudies-literature
Project description:The COVID-19 pandemic has impacted all aspects of our lives, including the information spread on social media. Prior literature has found that information diffusion dynamics on social networks mirror that of a virus, but applying the epidemic Susceptible-Infected-Removed model (SIR) model to examine how information spread is not sufficient to claim that information spreads like a virus. In this study, we explore whether there are similarities in the simulated SIR model (SIRsim), observed SIR model based on actual COVID-19 cases (SIRemp), and observed information cascades on Twitter about the virus (INFOcas) by using network analysis and diffusion modeling. We propose three primary research questions: (a) What are the diffusion patterns of COVID-19 virus spread, based on SIRsim and SIRemp? (b) What are the diffusion patterns of information cascades on Twitter (INFOcas), with respect to retweets, quote tweets, and replies? and (c) What are the major differences in diffusion patterns between SIRsim, SIRemp, and INFOcas? Our study makes a contribution to the information sciences community by showing how epidemic modeling of virus and information diffusion analysis of online social media are distinct but interrelated concepts.
| S-EPMC7645904 | biostudies-literature
Project description:BackgroundWith the World Health Organization's pandemic declaration and government-initiated actions against coronavirus disease (COVID-19), sentiments surrounding COVID-19 have evolved rapidly.ObjectiveThis study aimed to examine worldwide trends of four emotions-fear, anger, sadness, and joy-and the narratives underlying those emotions during the COVID-19 pandemic.MethodsOver 20 million social media twitter posts made during the early phases of the COVID-19 outbreak from January 28 to April 9, 2020, were collected using "wuhan," "corona," "nCov," and "covid" as search keywords.ResultsPublic emotions shifted strongly from fear to anger over the course of the pandemic, while sadness and joy also surfaced. Findings from word clouds suggest that fears around shortages of COVID-19 tests and medical supplies became increasingly widespread discussion points. Anger shifted from xenophobia at the beginning of the pandemic to discourse around the stay-at-home notices. Sadness was highlighted by the topics of losing friends and family members, while topics related to joy included words of gratitude and good health.ConclusionsOverall, global COVID-19 sentiments have shown rapid evolutions within just the span of a few weeks. Findings suggest that emotion-driven collective issues around shared public distress experiences of the COVID-19 pandemic are developing and include large-scale social isolation and the loss of human lives. The steady rise of societal concerns indicated by negative emotions needs to be monitored and controlled by complementing regular crisis communication with strategic public health communication that aims to balance public psychological wellbeing.
| S-EPMC7247466 | biostudies-literature
Project description:During national COVID-19 vaccine campaigns, people continuously engaged on Twitter to receive updates on the latest public health information, and to discuss and share their experiences. During this time, the spread of misinformation was widespread, which threatened the uptake of vaccines. It is therefore critical to understand the reasons behind vaccine misinformation and strategies to mitigate it. The current research aimed to understand the content of misleading tweets and the characteristics of their corresponding accounts. We performed a machine learning approach to identify misinformation in vaccine-related tweets, and calculated the demographic, engagement metrics and bot-like activities of corresponding accounts. We found critical periods where high amounts of misinformation coincided with important vaccine announcements, such as emergency approvals of vaccines. Moreover, we found Asian countries had a lower percentage of misinformation shared compared to Europe and North America. Our results showed accounts spreading misinformation had an overall 10% greater likelihood of bot activity and 15% more astroturf bot activity than accounts spreading accurate information. Furthermore, we found that accounts spreading misinformation had five times fewer followers and three times fewer verified badges than fact-sharing accounts. The findings of this study may help authorities to develop strategies to fight COVID-19 vaccine misinformation and improve vaccine uptake.
| S-EPMC10355402 | biostudies-literature
Project description:BackgroundWithin a few months, the COVID-19 pandemic had spread to many countries and had been a real challenge for health systems all around the world. This unprecedented crisis has led to a surge of online discussions about potential cures for the disease. Among them, vaccines have been at the heart of the debates and have faced lack of confidence before marketing in France.ObjectiveThis study aims to identify and investigate the opinions of French Twitter users on the announced vaccines against COVID-19 through sentiment analysis.MethodsThis study was conducted in 2 phases. First, we filtered a collection of tweets related to COVID-19 available on Twitter from February 2020 to August 2020 with a set of keywords associated with vaccine mistrust using word embeddings. Second, we performed sentiment analysis using deep learning to identify the characteristics of vaccine mistrust. The model was trained on a hand-labeled subset of 4548 tweets.ResultsA set of 69 relevant keywords were identified as the semantic concept of the word "vaccin" (vaccine in French) and focused mainly on conspiracies, pharmaceutical companies, and alternative treatments. Those keywords enabled us to extract nearly 350,000 tweets in French. The sentiment analysis model achieved 0.75 accuracy. The model then predicted 16% of positive tweets, 41% of negative tweets, and 43% of neutral tweets. This allowed us to explore the semantic concepts of positive and negative tweets and to plot the trends of each sentiment. The main negative rhetoric identified from users' tweets was that vaccines are perceived as having a political purpose and that COVID-19 is a commercial argument for the pharmaceutical companies.ConclusionsTwitter might be a useful tool to investigate the arguments for vaccine mistrust because it unveils political criticism contrasting with the usual concerns on adverse drug reactions. As the opposition rhetoric is more consistent and more widely spread than the positive rhetoric, we believe that this research provides effective tools to help health authorities better characterize the risk of vaccine mistrust.
| S-EPMC10132828 | biostudies-literature