Sentiment and emotion trends in nurses' tweets about the <scp>COVID</scp>‐19 pandemic

Xavier, Teenu; Lambert, Joshua

doi:10.1111/jnu.12775

Cited by 17 publications

(12 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The lexicon is a list of words, each associated with corresponding emotions (anger, fear, anticipation, trust, surprise, sadness, joy, and disgust) and sentiments (positive and negative); this classification is in line with the work of Plutchik and his wheel of emotions [ 22 ]. The NRC lexicon has been increasingly applied in recent years for quantifying affect in online textual data [ 23 – 25 ].…”

Section: Methodsmentioning

confidence: 99%

The emotional side of taking part in a cancer clinical trial

et al. 2023

View full text Add to dashboard Cite

Background Taking part in a cancer clinical trial often represents a source of psychological distress and emotional activation among patients and their caregivers. Nowadays, social media platforms provide a space for these groups to freely express and share their emotional experiences. Aims We aimed to reveal the most prevalent basic and complex emotions and sentiments in the posts of the patients and caregivers contemplating clinical trials on Reddit. Additionally, we aimed to categorize the types of users and posts. Methods With the use of keywords referring to clinical trials, we searched for public posts on the subreddit ‘cancer’. R studio v. 4.1.2 (2021-11-01) and NRC Emotion Lexicon was used for analysis. Following the theoretical framework of Plutchik’s wheel of emotions, the analysis included: 8 basic emotions (anger, fear, anticipation, trust, surprise, sadness, joy, and disgust) and 4 types of complex emotions (primary, secondary, tertiary, and opposite dyads). We utilized the package ‘PyPlutchik’ to visualize the emotion wheels in Python 3.10.5. Results A total of 241 posts were included in the final database. User types (129 patients, 112 caregivers) and post types (142 expressed shared experience, 77 expressed advice, and 85 conveyed both) were identified. Both positive (N = 2557, M = .68) and negative (N = 2154, M = .57) sentiments were high. The most prevalent basic emotions were: fear (N = 1702, M = .45), sadness (N = 1494, M = .40), trust (N = 1470, M = .44), and anticipation (N = 1376, M = .37). The prevalence of complex/dyadic emotions and their interpretation is further discussed. Conclusion In this contribution, we identified and discussed prevalent emotions such as fear, sadness, optimism, hope, despair, and outrage that mirror the psychological state of users and affect the medical choices they make. The insights gained in our study contribute to the understanding of the barriers and reinforcers to participation in trials and can improve the ability of healthcare professionals to assist patients when confronted with this choice.

show abstract

Section: Methodsmentioning

confidence: 99%

The emotional side of taking part in a cancer clinical trial

et al. 2023

View full text Add to dashboard Cite

show abstract

“…Participants' free text entries were processed and filtered using R software v 4.0.3, and the R package tidytext (version 0.4.1) was used to undertake the analysis. Three lexicons were cross referenced, namely AFINN, 13 Bing 14 and nrc 15 . The purpose was to create a surrogate marker for the impact of adverse events on patients.…”

Section: Methodsmentioning

confidence: 99%

Quantitative and qualitative analysis of individual experiences post botulinum toxin injection ‐ United Kingdom Survey

Zargaran

Sousi

et al. 2023

Skin Health and Disease

View full text Add to dashboard Cite

IntroductionIn the United Kingdom (UK), complications that arise following the administration of Botulinum Toxin are reported to the Medicines and Health Regulatory Agency (MHRA) via the Yellow Card Reporting Scheme. Over the past decade, there has been a significant increase in the number of non‐surgical aesthetic procedures. Concerns have been raised that the MHRA is not fully capturing complications in terms of volume and impact on patients.AimThis novel study explores the lived experiences of individuals who have experienced an adverse event following administration of Botulinum Toxin for aesthetic purposes. Using a combination of qualitative and quantitative methodologies, this analysis evaluates data relating to long‐lasting physical, psychological, emotional, and financial sequelae of complications arising from cosmetic Botulinum Toxin injections in the UK.MethodsA mixed method, qualitative and quantitative approach was adopted to gain comprehensive insights into patients' experiences. A focus group which comprised patient representatives, psychologists, and researchers reached a consensus on a 17‐question survey which was disseminated via social media channels. Deductive thematic analysis was used to analyse coded themes. Furthermore, for secondary analysis, sentiment analysis was used computationally as an innovative approach to identify and categorise free text responses associated with sentiments using natural language processing (NLP).ResultsIn the study, 655 responses were received, with 287 (44%) of respondents completing all questions. The mean age of respondents was 42.6 years old. 94.1% of respondents identified as female. In the sample, 79% of respondents reported an adverse event following their procedure, with the most common event being reported as ‘anxiety’. Findings revealed that 69% of respondents reported long‐lasting adverse effects. From the responses, 68.4% reported not having recovered physically, 63.5% of respondents stated that they had not recovered emotionally from complications, and 61.7% said that they have not recovered psychologically. In addition, 84% of respondents stated that they do not know who regulates the aesthetics industry. Furthermore, 92% of participants reported that their clinic or practitioner did not inform them about the Yellow Card Reporting Scheme. The sentiment analysis using the AFINN Lexicon yielded adjusted scores ranging from −3 to +2, with a mean value of −1.58.ConclusionThis is the largest survey in the UK completed by patients who experienced an adverse outcome following the aesthetic administration of Botulinum Toxin. Our study highlights the extent of the challenges faced by patients who experience an adverse event from physical, emotional, psychological, and financial perspectives. The lack of awareness of MHRA reporting structures and the lack of regulation within the UK's cosmetic injectables sector represent a significant public health challenge.

show abstract

“…29 proposed deep neural network model using 15, 000 COVID-19 related Turkish tweets to classify into positive, negative, and neutral sentiment and obtained 97.9% accuracy. 30 identified trends, sentiment and emotions in nurses' COVID-19 related tweets from March to December 2020 using using AFINN, Bing, and NRC lexicon and 31 also performed analysis on Turkish nurses tweets to identify public perspective at the time of COVID-19 in Turkey. In another study, 32 used the ML techniques to perform sentiment analysis on Twitter data from March 1st to April 21st , 2020 and followup in 33 with deepened analysis of public discourse and psychological reactions.…”

Section: Related Workmentioning

confidence: 99%

Reliable Knowledge Discovery from Social Media Data in Health Crisis in Developing Countries

Naseem¹,

Hassan²,

Naseer³

et al. 2022

Preprint

View full text Add to dashboard Cite

Social media is a barometer to anticipate sentiment of the public about the state of affairs and ongoing pandemic engaged an additional user base who are confined to their stations. COVID-19 startled the world and the crisis exacerbates in the absence of sufficient data for policy making. The data from social media and a timely analysis can provide sufficient statistics for decision-making. This study explores Twitter data to discover knowledgeable statistics on public sentiments about COVID-19 vaccination in developing countries. The study inspects data collected from two extremely populated developing countries: India and Pakistan. Support Vector Machine (SVM) classifier achieves 74.3% accuracy on the manually labeled dataset. Furthermore, the sentiment analysis is correlated with other indigenous factors like regional literacy rate and COVID-19 calamities in the time interval. It is observed that the negative to positive sentiments correlates with a lower to higher regional literacy rate and a higher COVID-19 intensity causes positive sentiments towards vaccination. The correlations of results with indigenous factors may help to advocate the devised strategies to the right audience and social media knowledge discovery with machine learning techniques may help to recover from data scarcity challenges in a medical emergency like COVID-19 in developing countries. Please note: Abbreviations should be introduced at the first mention in the main text – no abbreviations lists. Suggested structure of main text (not enforced) is provided below.

show abstract

Sentiment and emotion trends in nurses' tweets about the COVID‐19 pandemic

Cited by 17 publications

References 36 publications

The emotional side of taking part in a cancer clinical trial

The emotional side of taking part in a cancer clinical trial

Quantitative and qualitative analysis of individual experiences post botulinum toxin injection ‐ United Kingdom Survey

Reliable Knowledge Discovery from Social Media Data in Health Crisis in Developing Countries

Contact Info

Product

Resources

About