Using Twitter Data Analysis to Understand the Perceptions, Beliefs, and Attitudes about Pharmacotherapy Used in Rheumatology: An Observational Study.
Ontology highlight
ABSTRACT:
SUBMITTER: Abbasi-Perez A
PROVIDER: S-EPMC10252953 | biostudies-literature | 2023 May
REPOSITORIES: biostudies-literature
Publications
Using Twitter Data Analysis to Understand the Perceptions, Beliefs, and Attitudes about Pharmacotherapy Used in Rheumatology: An Observational Study.
Abbasi-Perez Adrian A Alvarez-Mon Miguel Angel MA Donat-Vargas Carolina C Ortega Miguel A MA Monserrat Jorge J Perez-Gomez Ana A Alvarez-Mon Melchor M
Healthcare (Basel, Switzerland) 20230523 11
Twitter has become an important platform for disseminating information about rheumatology drugs by patients, health professionals, institutions, and other users. The aim of this study was to analyze tweets related to 16 drugs used in rheumatology, including their volume, content, and type of user (patients, patients' relatives, health professionals, health institutions, pharmaceutical industry, general press, scientific journals and patients' associations), and to detect inappropriate medical co ...[more]
Similar Datasets
Project description:BackgroundElectroconvulsive therapy (ECT) is an effective and safe medical procedure that mainly indicated for depression, but is also indicated for patients with other conditions. However, ECT is among the most stigmatized and controversial treatments in medicine. Our objective was to examine social media contents on Twitter related to ECT to identify and evaluate public views on the matter.MethodsWe collected Twitter posts in English and Spanish mentioning ECT between January 1, 2019 and October 31, 2020. Identified tweets were subject to a mixed method quantitative-qualitative content and sentiment analysis combining manual and semi-supervised natural language processing machine-learning analyses. Such analyses identified the distribution of tweets, their public interest (retweets and likes per tweet), and sentiment for the observed different categories of Twitter users and contents.Results"Healthcare providers" users produced more tweets (25%) than "people with lived experience" and their "relatives" (including family members and close friends or acquaintances) (10% combined), and were the main publishers of "medical" content (mostly related to ECT's main indications). However, more than half of the total tweets had "joke or trivializing" contents, and such had a higher like and retweet ratio. Among those tweets manifesting personal opinions on ECT, around 75% of them had a negative sentiment.ConclusionsMixed method analysis of social media contents on Twitter offers a novel perspective to examine public opinion on ECT, and our results show attitudes more negative than those reflected in studies using surveys and other traditional methods.
| S-EPMC9970148 | biostudies-literature
Project description:ObjectiveUnderstanding public discourse on emergency use of unproven therapeutics is essential to monitor safe use and combat misinformation. We developed a natural language processing-based pipeline to understand public perceptions of and stances on coronavirus disease 2019 (COVID-19)-related drugs on Twitter across time.MethodsThis retrospective study included 609 189 US-based tweets between January 29, 2020 and November 30, 2021 on 4 drugs that gained wide public attention during the COVID-19 pandemic: (1) Hydroxychloroquine and Ivermectin, drug therapies with anecdotal evidence; and (2) Molnupiravir and Remdesivir, FDA-approved treatment options for eligible patients. Time-trend analysis was used to understand the popularity and related events. Content and demographic analyses were conducted to explore potential rationales of people's stances on each drug.ResultsTime-trend analysis revealed that Hydroxychloroquine and Ivermectin received much more discussion than Molnupiravir and Remdesivir, particularly during COVID-19 surges. Hydroxychloroquine and Ivermectin were highly politicized, related to conspiracy theories, hearsay, celebrity effects, etc. The distribution of stance between the 2 major US political parties was significantly different (P < .001); Republicans were much more likely to support Hydroxychloroquine (+55%) and Ivermectin (+30%) than Democrats. People with healthcare backgrounds tended to oppose Hydroxychloroquine (+7%) more than the general population; in contrast, the general population was more likely to support Ivermectin (+14%).ConclusionOur study found that social media users with have different perceptions and stances on off-label versus FDA-authorized drug use across different stages of COVID-19, indicating that health systems, regulatory agencies, and policymakers should design tailored strategies to monitor and reduce misinformation for promoting safe drug use. Our analysis pipeline and stance detection models are made public at https://github.com/ningkko/COVID-drug.
| S-EPMC9278189 | biostudies-literature
Project description:BackgroundWith the increasing popularity of snus, it is essential to understand the public perception of this oral tobacco product. Twitter-a popular social media platform that is being used to share personal experiences and opinions-provides an ideal data source for studying the public perception of snus.ObjectiveThis study aims to examine public perceptions and discussions of snus on Twitter.MethodsTwitter posts (tweets) about snus were collected through the Twitter streaming application programming interface from March 11, 2021, to February 26, 2022. A temporal analysis was conducted to examine the change in number of snus-related tweets over time. A sentiment analysis was conducted to examine the sentiments of snus-related tweets. Topic modeling was applied to tweets to determine popular topics. Finally, a keyword search and hand-coding were used to understand the health symptoms mentioned in snus-related tweets.ResultsThe sentiment analysis showed that the proportion of snus-related tweets with a positive sentiment was significantly higher than the proportion of negative sentiment tweets (4341/11,631, 37.32% vs 3094/11,631, 26.60%; P<.001). The topic modeling analysis revealed that positive tweets focused on snus's harm reduction and snus use being an alternative to smoking, while negative tweets focused on health concerns related to snus. Mouth and respiratory symptoms were the most mentioned health symptoms in snus-related tweets.ConclusionsThis study examined the public perception of snus and popular snus-related topics discussed on Twitter, thus providing a guide for policy makers with regard to the future formulation and adjustment of tobacco regulation policies.
| S-EPMC9468913 | biostudies-literature
Project description:BackgroundThe emergence of SARS-CoV-2 (ie, COVID-19) has given rise to a global pandemic affecting 215 countries and over 40 million people as of October 2020. Meanwhile, we are also experiencing an infodemic induced by the overabundance of information, some accurate and some inaccurate, spreading rapidly across social media platforms. Social media has arguably shifted the information acquisition and dissemination of a considerably large population of internet users toward higher interactivities.ObjectiveThis study aimed to investigate COVID-19-related health beliefs on one of the mainstream social media platforms, Twitter, as well as potential impacting factors associated with fluctuations in health beliefs on social media.MethodsWe used COVID-19-related posts from the mainstream social media platform Twitter to monitor health beliefs. A total of 92,687,660 tweets corresponding to 8,967,986 unique users from January 6 to June 21, 2020, were retrieved. To quantify health beliefs, we employed the health belief model (HBM) with four core constructs: perceived susceptibility, perceived severity, perceived benefits, and perceived barriers. We utilized natural language processing and machine learning techniques to automate the process of judging the conformity of each tweet with each of the four HBM constructs. A total of 5000 tweets were manually annotated for training the machine learning architectures.ResultsThe machine learning classifiers yielded areas under the receiver operating characteristic curves over 0.86 for the classification of all four HBM constructs. Our analyses revealed a basic reproduction number R0 of 7.62 for trends in the number of Twitter users posting health belief-related content over the study period. The fluctuations in the number of health belief-related tweets could reflect dynamics in case and death statistics, systematic interventions, and public events. Specifically, we observed that scientific events, such as scientific publications, and nonscientific events, such as politicians' speeches, were comparable in their ability to influence health belief trends on social media through a Kruskal-Wallis test (P=.78 and P=.92 for perceived benefits and perceived barriers, respectively).ConclusionsAs an analogy of the classic epidemiology model where an infection is considered to be spreading in a population with an R0 greater than 1, we found that the number of users tweeting about COVID-19 health beliefs was amplifying in an epidemic manner and could partially intensify the infodemic. It is "unhealthy" that both scientific and nonscientific events constitute no disparity in impacting the health belief trends on Twitter, since nonscientific events, such as politicians' speeches, might not be endorsed by substantial evidence and could sometimes be misleading.
| S-EPMC7901597 | biostudies-literature
Project description:BackgroundIn the United States, racial disparities in birth outcomes persist and have been widening. Interpersonal and structural racism are leading explanations for the continuing racial disparities in birth outcomes, but research to confirm the role of racism and evaluate trends in the impact of racism on health outcomes has been hampered by the challenge of measuring racism. Most research on discrimination relies on self-reported experiences of discrimination, and few studies have examined racial attitudes and bias at the US national level.ObjectiveThis study aimed to investigate the associations between state-level Twitter-derived sentiments related to racial or ethnic minorities and birth outcomes.MethodsWe utilized Twitter's Streaming application programming interface to collect 26,027,740 tweets from June 2015 to December 2017, containing at least one race-related term. Sentiment analysis was performed using support vector machine, a supervised machine learning model. We constructed overall indicators of sentiment toward minorities and sentiment toward race-specific groups. For each year, state-level Twitter-derived sentiment data were merged with birth data for that year. The study participants were women who had singleton births with no congenital abnormalities from 2015 to 2017 and for whom data were available on gestational age (n=9,988,030) or birth weight (n=9,985,402). The main outcomes were low birth weight (birth weight ≤2499 g) and preterm birth (gestational age <37 weeks). We estimated the incidence ratios controlling for individual-level maternal characteristics (sociodemographics, prenatal care, and health behaviors) and state-level demographics, using log binomial regression models.ResultsThe accuracy for identifying negative sentiments on comparing the machine learning model to manually labeled tweets was 91%. Mothers living in states in the highest tertile for negative sentiment tweets referencing racial or ethnic minorities had greater incidences of low birth weight (8% greater, 95% CI 4%-13%) and preterm birth (8% greater, 95% CI 0%-14%) compared with mothers living in states in the lowest tertile. More negative tweets referencing minorities were associated with adverse birth outcomes in the total population, including non-Hispanic white people and racial or ethnic minorities. In stratified subgroup analyses, more negative tweets referencing specific racial or ethnic minority groups (black people, Middle Eastern people, and Muslims) were associated with poor birth outcomes for black people and minorities.ConclusionsA negative social context related to race was associated with poor birth outcomes for racial or ethnic minorities, as well as non-Hispanic white people.
| S-EPMC7381033 | biostudies-literature
Project description:BackgroundNonpharmaceutical interventions (NPIs) (such as wearing masks and social distancing) have been implemented by governments around the world to slow the spread of COVID-19. To promote public adherence to these regimes, governments need to understand the public perceptions and attitudes toward NPI regimes and the factors that influence them. Twitter data offer a means to capture these insights.ObjectiveThe objective of this study is to identify tweets about COVID-19 NPIs in six countries and compare the trends in public perceptions and attitudes toward NPIs across these countries. The aim is to identify factors that influenced public perceptions and attitudes about NPI regimes during the early phases of the COVID-19 pandemic.MethodsWe analyzed 777,869 English language tweets about COVID-19 NPIs in six countries (Australia, Canada, New Zealand, Ireland, the United Kingdom, and the United States). The relationship between tweet frequencies and case numbers was assessed using a Pearson correlation analysis. Topic modeling was used to isolate tweets about NPIs. A comparative analysis of NPIs between countries was conducted.ResultsThe proportion of NPI-related topics, relative to all topics, varied between countries. The New Zealand data set displayed the greatest attention to NPIs, and the US data set showed the lowest. The relationship between tweet frequencies and case numbers was statistically significant only for Australia (r=0.837, P<.001) and New Zealand (r=0.747, P<.001). Topic modeling produced 131 topics related to one of 22 NPIs, grouped into seven NPI categories: Personal Protection (n=15), Social Distancing (n=9), Testing and Tracing (n=10), Gathering Restrictions (n=18), Lockdown (n=42), Travel Restrictions (n=14), and Workplace Closures (n=23). While less restrictive NPIs gained widespread support, more restrictive NPIs were perceived differently across countries. Four characteristics of these regimes were seen to influence public adherence to NPIs: timeliness of implementation, NPI campaign strategies, inconsistent information, and enforcement strategies.ConclusionsTwitter offers a means to obtain timely feedback about the public response to COVID-19 NPI regimes. Insights gained from this analysis can support government decision making, implementation, and communication strategies about NPI regimes, as well as encourage further discussion about the management of NPI programs for global health events, such as the COVID-19 pandemic.
| S-EPMC7505256 | biostudies-literature
Project description:BackgroundNicotine is a highly addictive agent in tobacco products. On June 21, 2022, the US Food and Drug Administration (FDA) announced a plan to propose a rule to establish a maximum nicotine level in cigarettes and other combusted tobacco products.ObjectiveThis study aimed to understand public perception and discussion of very low nicotine content (VLNC) on Twitter (rebranded as X in July 2023).MethodsFrom December 12, 2021, to January 1, 2023, we collected Twitter data using relevant keywords such as "vln," "low nicotine," and "reduced nicotine." After a series of preprocessing steps (such as removing duplicates, retweets, and commercial tweets), we identified 3270 unique noncommercial tweets related to VLNC. We used an inductive method to assess the public perception and discussion of VLNC on Twitter. To establish a codebook, we randomly selected 300 tweets for hand-coding, including the attitudes (positive, neutral, and negative) toward VLNC (including its proposed rule) and major topics (13 topics). The Cohen κ statistic between the 2 human coders reached over 70%, indicating a substantial interrater agreement. The rest of the tweets were single-coded according to the codebook.ResultsWe observed a significant peak in the discussion of VLNC on Twitter within 4 days of the FDA's announcement of the proposed rule on June 21, 2022. The proportion of tweets with a negative attitude toward VLNC was significantly lower than those with a positive attitude, 24.5% (801/3270) versus 37.09% (1213/3270) with P<.001 from the 2-proportion z test. Among tweets with a positive attitude, the topic "Reduce cigarette consumption or help smoking cessation" was dominant (1097/1213, 90.44%). Among tweets with a negative attitude, the topic "VLNC leads to more smoking" was the most popular topic (227/801, 28.34%), followed by "Similar toxicity of VLNC as a regular cigarette" (223/801, 27.84%), and "VLNC is not a good method for quitting smoking" (211/801, 26.34%).ConclusionsThere is a more positive attitude toward VLNC than a negative attitude on Twitter, resulting from different opinions about VLNC. Discussions around VLNC mainly focused on whether VLNC could help people quit smoking.
| S-EPMC11656502 | biostudies-literature
Project description:The US Preventive Services Task Force recommends that smokers aged 55-80 should be screened annually with low-dose computed tomography (LDCT). This study identified demographics, smoking history, health risk perceptions, knowledge, and attitudes factors of older smokers (≥55 years) related to LDCT agreement. Using binary logistic regression, a predictive model of factors to explain LDCT agreement was produced. This is a cross-sectional, national, online survey of 338 older smokers (≥55 years) with a ≥30 pack-year smoking history. Over 82% of the sample believed that a person who continues to smoke after the age of 40 has at least a 25% chance of developing lung cancer and 77.3% would "agree to a LDCT today". Using chi-square analyses, six variables that were significant at the 0.10 level were selected for inclusion in model development. Four of the independent variables made a unique statistically significant contribution to the model: perceives accuracy of the LDCT as an important factor in the decision to have a LDCT scan; believes that early detection of LC will result in a good prognosis; believes that they are at high risk for lung cancer; and is not afraid of CT scans. Of note, only 10.9% believed that a negative CT scan result would mean that they could continue to smoke. Older smokers are aware of the risks of smoking, are interested in smoking cessation, and most are interested in and positive about LDCT. Cognitive aspects of participation in screening are key to increasing the uptake of lung cancer screening among high-risk smokers.
| S-EPMC4831294 | biostudies-literature
Project description:BackgroundChronic fatigue syndrome (CFS) is a debilitating illness with particular difficulties for healthcare providers because there are no diagnostic signs or laboratory tests and because management aims to merely improve symptoms. Further complicating management, healthcare providers' awareness concerning CFS has not been rigorously assessed. The present study aimed to ascertain United States (U.S.) healthcare providers' awareness of CFS and to assess their knowledge, attitudes, and beliefs (KAB) related to diagnosis and management of the illness. This information forms the foundation for developing CFS educational strategies.MethodsWe combined convenience and probability samples to measure CFS KAB among healthcare providers. In the convenience sample, 1,255 healthcare providers (81% response rate) from 13 professional conferences completed a 12-item form. Descriptive statistics were reported for 9 KAB item responses and chi-square tests were performed for examining their association with giving a diagnosis of CFS. We used principal component analysis to construct multidimensional subscales and perform a general linear model to examine factors associated with subscales. The probability sample involved data on 15 CFS-specific questions from 2006 and 2007 DocStyles web-based panel surveys collected from 2,750 physicians (average response rate 55%). We calculated descriptive and chi-square statistics. The significance was set at two-tailed with the alpha level of 0.05.ResultsHealthcare providers in both samples were aware of CFS and exhibited a high level of knowledge. Overall, 96% of respondents in the DocStyles (probability) sample had heard about CFS. Healthcare providers in the conference (convenience) sample demonstrated good KAB scores; physicians' scores were highest on KAB scales and lowest in perception. Nurses' scores were lowest in knowledge. More than 40% of physicians reported ever giving a CFS diagnosis and in the DocStyles (probability) sample more than 80% of physicians correctly identified CFS symptoms. Physicians reported professional journals, the Internet, and continuing education programs as the top 3 sources from which they obtain CFS information.ConclusionsFindings from these combined samples fill a gap in the evidence-base of U.S. healthcare providers' and knowledge, attitudes, and beliefs concerning CFS. Importantly, respondents in both samples expressed similar knowledge, attitudes, beliefs and perceptions. Awareness was high and negative attitudes were low. The primary areas for future education should address diagnosis and management of CFS and should be delivered through those venues providers indicated they primarily use. Data from this study provide a benchmark for evaluation the success of these future efforts.
| S-EPMC2875206 | biostudies-literature
Project description:BackgroundSocial media plays a pivotal role in disseminating news globally and acts as a platform for people to express their opinions on various topics. A wide variety of views accompany COVID-19 vaccination drives across the globe, often colored by emotions that change along with rising cases, approval of vaccines, and multiple factors discussed online.ObjectiveThis study aims to analyze the temporal evolution of different emotions and the related influencing factors in tweets belonging to 5 countries with vital vaccine rollout programs, namely India, the United States, Brazil, the United Kingdom, and Australia.MethodsWe extracted a corpus of nearly 1.8 million Twitter posts related to COVID-19 vaccination and created 2 classes of lexical categories-emotions and influencing factors. Using cosine distance from selected seed words' embeddings, we expanded the vocabulary of each category and tracked the longitudinal change in their strength from June 2020 to April 2021 in each country. Community detection algorithms were used to find modules in positive correlation networks.ResultsOur findings indicated the varying relationship among emotions and influencing factors across countries. Tweets expressing hesitancy toward vaccines represented the highest mentions of health-related effects in all countries, which reduced from 41% to 39% in India. We also observed a significant change (P<.001) in the linear trends of categories like hesitation and contentment before and after approval of vaccines. After the vaccine approval, 42% of tweets coming from India and 45% of tweets from the United States represented the "vaccine_rollout" category. Negative emotions like rage and sorrow gained the highest importance in the alluvial diagram and formed a significant module with all the influencing factors in April 2021, when India observed the second wave of COVID-19 cases.ConclusionsBy extracting and visualizing these tweets, we propose that such a framework may help guide the design of effective vaccine campaigns and be used by policy makers to model vaccine uptake and targeted interventions.
| S-EPMC10165720 | biostudies-literature