Using Twitter Comments to Understand People's Experiences of UK Health Care During the COVID-19 Pandemic: Thematic and Sentiment Analysis

Intersecting factors of disadvantage and discrimination and their effect on daily life during the coronavirus pandemic: the CICADA-ME mixed-methods study

Background

The COVID-19 pandemic exacerbated pre-existing societal inequities. Our study addresses the dearth of studies on how intersecting factors of disadvantage and discrimination affected pandemic daily life for disabled people from minoritised ethnic groups, aiming to improve their experiences and social, health and well-being outcomes.

Objectives

Through an intersectionality lens, to: explore and compare, by location and time, survey and qualitative data on changing needs for social, health and well-being outcomes relate coping strategies/solutions to these explore formal and informal network issues/affordances gain insights from synthesising our data contextualise and explore transferability of findings co-create outputs with stakeholders.

Design

Mixed-methods, asset-based, underpinned by embodiment disability models and intersectionality, integrating three strands: (secondary): analysis of existing cohort/panel data, literature review (primary: quantitative): new survey (n = 4326), three times over 18 months (primary: qualitative): semistructured interviews (n = 271), interviewee co-create workshops (n = 104) 5 and 10 months later, mixed stakeholder co-design workshops (n = 30) for rapid-impact solutions to issues, key informant interviews (n = 4).

Setting

United Kingdom and Republic of Ireland.

Participants

Strand 2: community-dwelling migrants, White British comparators, with/without disability. Strand 3: focus on Arab, South Asian, African, Central/East European, or White British heritage with/without disability.

Results

We found strong adherence to pandemic restrictions (where accommodation, economic situations and disability allowed) due to COVID-19 vulnerabilities. High vaccine hesitancy (despite eventual uptake) resulted from side-effect concerns and (mis)trust in the government. Many relied on food banks, local organisations, communities and informal networks. Pandemic-related income loss was common, particularly affecting undocumented migrants. Participants reported a crisis in mental health care, non-holistic social and housing care, and inaccessible, poor-quality and discriminatory remote health/social care. They preferred private care (which they could not easily afford), community or self-help online support. Lower socioeconomic status, mental health and mobility issues reduced well-being. Individual and community assets and coping strategies mitigated some issues, adapted over different pandemic phases, and focused on empowerment, self-reflection, self-care and social connectivity. Technology needs cut across these.

Limitations

We could not explore area-level social distancing and infection rates. Data collection was largely online, possibly excluding some older, digitally deprived or more disabled participants. Participants engaged differently in online and face-to-face co-create workshops. Our qualitative data over-represent England and South Asian people and use contestable categories.

Conclusions

Different intersecting factors led to different experiences, with low socioeconomic status particularly significant. Overall, disability and minoritised ethnic identities led to worse pandemic experiences. Our co-design work shows how to build on the assets and strengths; simple changes in professional communication and understanding should improve experience. Minoritised groups can easily be involved in policy and practice decision-making, reducing marginalisation, with better сare and outcomes.

Future work

More research is needed on: (1) the impact of the post-pandemic economic situation and migration policies on migrant mental health/well-being; (2) supporting empowerment strategies across disadvantaged intersecting identities; and (3) technological deprivation and the cultural and disability-relevant acceptability of remote consultations. We found some differences in the devolved nations, which need elucidation.

Study registration

This study is registered as ISRCTN40370, PROSPERO CRD42021262590 and CRD42022355254.

Funding

This award was funded by the National Institute for Health and Care Research (NIHR) Health and Social Care Delivery Research programme (NIHR award ref: NIHR132914) and is published in full in Health and Social Care Delivery Research; Vol. 13, No. 2. See the NIHR Funding and Awards website for further award information.

Mining social media data to inform public health policies: a sentiment analysis case study

In the face of growing health challenges, nontraditional sources of data, such as open data, have the potential to transform how decisions are made and used to inform public health policies. Focusing on the COVID-19 pandemic, this article presents a case study employing sentiment analysis on unstructured social media data from Twitter (now X) to gauge public sentiment regarding pandemic-related restrictions. Our study aims to uncover and analyze Jamaican citizens' emotions and opinions surrounding COVID-19 restrictions following an outbreak at a call center in April 2020. Machine learning sentiment analysis was used to analyze tweets from Twitter related to the lockdown. A total of 1 609 tweets were retrieved and analyzed, 76% of which expressed negative sentiments, suggesting that the majority of citizens were not in favor of the restrictions. The low compliance with the government-mandated policy may be related to the high percentage of negative sentiments expressed. Insights from citizens' sentiments derived from open data sources such as Twitter can serve as valuable indicators for public health policymakers, providing critical input that will aid in tailoring interventions that align with public sentiments, thereby enhancing the effectiveness of and compliance with public health policies. This type of analysis can be useful to the health community and more generally to governments, as it allows for a more scientific assessment of public response to public health intervention techniques in real time. This study contributes to the emerging discourse on the integration of nontraditional data into public health policy-making, highlighting the growing potential for the use of these novel analytic techniques in addressing complex public health challenges.

Sentiment analysis of post-COVID-19 health information needs of autism spectrum disorder community: insights from social media discussions

OBJECTIVE

This study explores the health information needs of individuals with autism spectrum disorder (ASD) and their caregivers in the post-COVID-19 era by analyzing discussions from Reddit, a popular social media platform.

METHODS

Utilizing a mixed-method approach that integrates qualitative content analysis with quantitative sentiment analysis, we analyzed user-generated content from the "r/autism" subreddit to identify recurring themes and sentiments.

RESULTS

The qualitative analysis uncovered key themes, including symptoms, diagnostic challenges, caregiver experiences, treatment options, and stigma, reflecting the diverse concerns within the ASD community. The quantitative sentiment analysis revealed a predominance of positive sentiment across discussions, although significant instances of neutral and negative sentiments were also present, indicating varied experiences and perspectives among community members. Among the machine learning models used for sentiment classification, the Bi-directional Long Short-Term Memory (Bi-LSTM) model achieved the highest performance, demonstrating a validation accuracy of 95.74%.

CONCLUSIONS

The findings highlight the need for improved digital platforms and community resources to address the specific health information needs of the ASD community, particularly in enhancing access to reliable information and fostering supportive environments. These insights can guide future interventions and policies aimed at improving the well-being of autistic persons and their caregivers.

Exploring the Impact of the COVID-19 Pandemic on Twitter in Japan: Qualitative Analysis of Disrupted Plans and Consequences

BACKGROUND

Despite being a pandemic, the impact of the spread of COVID-19 extends beyond public health, influencing areas such as the economy, education, work style, and social relationships. Research studies that document public opinions and estimate the long-term potential impact after the pandemic can be of value to the field.

OBJECTIVE

This study aims to uncover and track concerns in Japan throughout the COVID-19 pandemic by analyzing Japanese individuals' self-disclosure of disruptions to their life plans on social media. This approach offers alternative evidence for identifying concerns that may require further attention for individuals living in Japan.

METHODS

We extracted 300,778 tweets using the query phrase Corona-no-sei ("due to COVID-19," "because of COVID-19," or "considering COVID-19"), enabling us to identify the activities and life plans disrupted by the pandemic. The correlation between the number of tweets and COVID-19 cases was analyzed, along with an examination of frequently co-occurring words.

RESULTS

The top 20 nouns, verbs, and noun plus verb pairs co-occurring with Corona no-sei were extracted. The top 5 keywords were graduation ceremony, cancel, school, work, and event. The top 5 verbs were disappear, go, rest, can go, and end. Our findings indicate that education emerged as the top concern when the Japanese government announced the first state of emergency. We also observed a sudden surge in anxiety about material shortages such as toilet paper. As the pandemic persisted and more states of emergency were declared, we noticed a shift toward long-term concerns, including careers, social relationships, and education.

CONCLUSIONS

Our study incorporated machine learning techniques for disease monitoring through the use of tweet data, allowing the identification of underlying concerns (eg, disrupted education and work conditions) throughout the 3 stages of Japanese government emergency announcements. The comparison with COVID-19 case numbers provides valuable insights into the short- and long-term societal impacts, emphasizing the importance of considering citizens' perspectives in policy-making and supporting those affected by the pandemic, particularly in the context of Japanese government decision-making.

Twitter users perceptions of AI-based e-learning technologies

Today, teaching and learning paths increasingly intersect with technologies powered by emerging artificial intelligence (AI).This work analyses public opinions and sentiments about AI applications that affect e-learning, such as ChatGPT, virtual and augmented reality, microlearning, mobile learning, adaptive learning, and gamification. The way people perceive technologies fuelled by artificial intelligence can be tracked in real time in microblog messages promptly shared by Twitter users, who currently constitute a large and ever-increasing number of individuals. The observation period was from November 30, 2022, the date on which ChatGPT was launched, to March 31, 2023. A two-step sentiment analysis was performed on the collected English-language tweets to determine the overall sentiments and emotions. A latent Dirichlet allocation model was built to identify commonly discussed topics in tweets. The results show that the majority of opinions are positive. Among the eight emotions of the Syuzhet package, 'trust' and 'joy' are the most common positive emotions observed in the tweets, while 'fear' is the most common negative emotion. Among the most discussed topics with a negative outlook, two particular aspects of fear are identified: an 'apocalyptic-fear' that artificial intelligence could lead the end of humankind, and a fear for the 'future of artistic and intellectual jobs' as AI could not only destroy human art and creativity but also make the individual contributions of students and researchers not assessable. On the other hand, among the topics with a positive outlook, trust and hope in AI tools for improving efficiency in jobs and the educational world are identified. Overall, the results suggest that AI will play a significant role in the future of the world and education, but it is important to consider the potential ethical and social implications of this technology. By leveraging the positive aspects of AI while addressing these concerns, the education system can unlock the full potential of this emerging technology and provide a better learning experience for students.

Impact of COVID-19 on patient experience of kidney care: a rapid review

INTRODUCTION

In March 2020, a pandemic state was declared due to SARS-COV-2 (COVID-19). Patients with kidney disease, especially those on replacement therapies, proved more susceptible to severe infection. This rapid literature review aims to help understand how the pandemic impacted patient experience of kidney care.

METHODS

It was conducted in accordance with Cochrane Rapid Review interim guidance. Search terms, 'coronavirus', 'kidney care', and 'patient-reported experience' and terms with similar semantic meaning, identified 1,117 articles in Medline, Scopus, and Worldwide Science. Seventeen were included in the narrative synthesis.

RESULTS

The findings were summarised into three themes: remote consultation and telemedicine (n = 9); psychosocial impact (n = 2); and patient satisfaction and patient-reported experience (n = 6). Patients were mostly satisfied with remote consultations, describing them as convenient and allowing avoidance of hospital visits. Anxieties included missing potentially important clinical findings due to lack of physical examination, poor digital literacy, and technical difficulties. Psychosocial impact differed between treatment modalities-transplant recipients expressing feelings of instability and dread of having to return to dialysis, and generally, were less satisfied, citing reduced ability to work and difficulty accessing medications. Those on home dialysis treatments tended to feel safer. Findings focused on aspects of patient experience of kidney care during the pandemic rather than a holistic view.

CONCLUSIONS

There was little direct evaluation of modality differences and limited consideration of health inequalities in care experiences. A fuller understanding of these issues would guide policy agendas to support patient experience during future public health crises.

How do Twitter users feel about telehealth? A mixed-methods analysis of experiences, perceptions and expectations

BACKGROUND

Telehealth use has increased considerably in the last years and evidence suggests an overall positive sentiment towards telehealth. Twitter has a wide userbase and can enrich our understanding of telehealth use by users expressing their personal opinions in an unprompted way. This study aimed to explore Twitter users' experiences, perceptions and expectations about telehealth over the last 5 years.

METHODS

Mixed-methods study with sequential complementary quantitative and qualitative phases was used for analysis stages comprising (1) a quantitative semiautomated analysis and (2) a qualitative research-led thematic analysis. A machine learning model was used to establish the data set with relevant English language tweets from 1 September 2017 to 1 September 2022 relating to telehealth using predefined search words. Results were integrated at the end.

RESULTS

From the initial 237,671 downloaded tweets, 6469 had a relevancy score above 0.8 and were input into Leximancer and 595 were manually analysed. Experiences, perceptions and expectations were categorised into three domains: experience with telehealth consultation, telehealth changes over time and the purpose of the appointment. The most tweeted experience was expectations for telehealth consultation in comparison to in-person consultations. Users mostly mentioned the hope that waiting times for the consultations to start to be less than in-person, more telehealth appointments to be available and telehealth to be cheaper. Perceptions around the use of telehealth in relation to healthcare delivery changes brought about by the COVID-19 pandemic were also expressed. General practitioners were mentioned six times more than other healthcare professionals.

CONCLUSION/IMPLICATIONS

This study found that Twitter users expect telehealth services to be better, more affordable and more available than in-person consultations. Users acknowledged the convenience of not having to travel for appointments and the challenges to adapt to telehealth.

PATIENT OR PUBLIC CONTRIBUTION

An open data set with 237,671 tweets expressing users' opinions in an unprompted way was used as a source for telehealth service users, caregivers and members of the public experiences, perceptions and expectations of telehealth.

Exploring Discussions About Virtual Reality on Twitter to Inform Brain Injury Rehabilitation: Content and Network Analysis

BACKGROUND

Virtual reality (VR) use in brain injury rehabilitation is emerging. Recommendations for VR development in this field encourage end user engagement to determine the benefits and challenges of VR use; however, existing literature on this topic is limited. Data from social networking sites such as Twitter may further inform development and clinical practice related to the use of VR in brain injury rehabilitation.

OBJECTIVE

This study collected and analyzed VR-related tweets to (1) explore the VR tweeting community to determine topics of conversation and network connections, (2) understand user opinions and experiences of VR, and (3) identify tweets related to VR use in health care and brain injury rehabilitation.

METHODS

Publicly available tweets containing the hashtags #virtualreality and #VR were collected up to twice weekly during a 6-week period from July 2020 to August 2020 using NCapture (QSR International). The included tweets were analyzed using mixed methods. All tweets were coded using inductive content analysis. Relevant tweets (ie, coded as "VR in health care" or "talking about VR") were further analyzed using Dann's content coding. The biographies of users who sent relevant tweets were examined descriptively. Tweet data networks were visualized using Gephi computational analysis.

RESULTS

A total of 260,715 tweets were collected, and 70,051 (26.87%) were analyzed following eligibility screening. The sample comprised 33.68% (23,596/70,051) original tweets and 66.32% (46,455/70,051) retweets. Content analysis generated 10 main categories of original tweets related to VR (ie, advertising and promotion, VR content, talking about VR, VR news, general technology, VR industry, VR live streams, VR in health care, VR events, and VR community). Approximately 4.48% (1056/23,596) of original tweets were related to VR use in health care, whereas 0.19% (45/23,596) referred to VR in brain injury rehabilitation. In total, 14.86% (3506/23,596) of original tweets featured commentary on user opinions and experiences of VR applications, equipment, and software. The VR tweeting community comprised a large network of 26,001 unique Twitter users. Users that posted tweets related to "VR in health care" (2124/26,001, 8.17%) did not form an interconnected VR network, whereas many users "talking about VR" (3752/26,001, 14.43%) were connected within a central network.

CONCLUSIONS

This study provides valuable data on community-based experiences and opinions related to VR. Tweets showcased various VR applications, including in health care, and identified important user-based considerations that can be used to inform VR use in brain injury rehabilitation (eg, technical design, accessibility, and VR sickness). Limited discussions and small user networks related to VR in brain injury rehabilitation reflect the paucity of literature on this topic and the potential underuse of this technology. These findings emphasize that further research is required to understand the specific needs and perspectives of people with brain injuries and clinicians regarding VR use in rehabilitation.

General practitioners' wellbeing during the COVID-19 pandemic: Novel methods with social media data

BACKGROUND

It is difficult to engage busy healthcare professionals in research. Yet during the COVID-19 pandemic, gaining their perspectives has never been more important.

OBJECTIVE

To explore social media data for insights into the wellbeing of UK General Practitioners (GPs) during the Covid-19 pandemic.

METHODS

We used a combination of search approaches to identify 381 practising UK NHS GPs on Twitter. Using a two stage social media analysis, we firstly searched for key themes from 91,034 retrieved tweets (before and during the pandemic). Following this we used qualitative content analysis to provide in-depth insights from 7145 tweets related to wellbeing.

RESULTS

Social media proved a useful tool to identify a cohort of UK GPs; following their tweets longitudinally to explore key themes and trends in issues related to GP wellbeing during the pandemic. These predominately related to support, resources and public perceptions and fluctuations were identified at key timepoints during the pandemic, all achieved without burdening busy GPs.

CONCLUSION

Social media data can be searched to identify a cohort of GPs to explore their wellbeing and changes over time.

Knowledge sharing of health technology among clinicians in integrated care system: The role of social networks

Promoting clinicians' knowledge sharing of appropriate health technology within the integrated care system (ICS) is of great vitality in bridging the technological gap between member institutions. However, the role of social networks in knowledge sharing of health technology is still largely unknown. To address this issue, the study aims to clarify the influence of clinicians' social networks on knowledge sharing of health technology within the ICS. A questionnaire survey was conducted among the clinicians in the Alliance of Liver Disease Specialists in Fujian Province, China. Social network analysis was conducted using NetDraw and UCINET, and the quadratic assignment procedure (QAP) multiple regression was used to analyze the influencing factors of knowledge sharing of health technology. The results showed that the ICS played an insufficient role in promoting overall knowledge sharing, especially inter-institutional knowledge sharing. Trust, emotional support, material support, and cognitive proximity positively influenced knowledge sharing of health technology, while the frequency of interaction and relationship importance had a negative impact on it. The finding extended the research scope of social network theory to the field of healthcare and will bridge the evidence gap in the influence of the clinicians' social networks on their knowledge sharing within the ICS, providing new ideas to boost knowledge sharing and diffusion of appropriate health technology.

A comparative geolocation and text mining analysis of emotions and topics during the COVID-19 Pandemic in the UK

BACKGROUND

In recent years, the COVID-19 pandemic has brought great changes to public health, society and the economy. Social media provides a platform for people to discuss health concerns, living conditions and policies during the epidemic, which allows policy makers to use its contents to analyse the public emotions and attitudes for decision making.

OBJECTIVE

In this study, we aim to use deep learning-based methods to understand public emotions on topics related to the COVID-19 pandemic in the UK through a comparative geolocation and text mining analysis on Twitter.

METHODS

Over 500,000 tweets related to COVID-19 from 48 different cities in the UK were extracted, and the data cover the period of the last 2 years (from February 2020 to November 2021). We leveraged three advanced deep learning-based models: SenticNet 6 for sentiment analysis, SpanEmo for emotion recognition, and Combined Topic Modelling (CTM) for topic modelling to geospatially analyse the sentiment, emotion and topics of tweets in the UK.

RESULTS

According to the analysis, we observed a significant change in the number of tweets as the epidemiological situation and vaccination these two years. There was a sharp increase in the number of tweets from January 2020 to February 2020 due to the outbreak of COVID-19 in the UK. Then, the number of tweets gradually declined from February 2020. Moreover, with the identification of the COVID-19 Omicron variant in the UK in November 2021, the number of tweets grew. Our findings reveal people's attitudes and emotions towards topics related to COVID-19. For sentiment, about 60% of tweets are positive, 20% neutral and 20% are negative. For emotion, people tend to express highly positive emotions in the beginning of 2020, while expressing highly negative emotions as the time changes towards the end of 2021. The topics are also changing during the pandemic.

CONCLUSIONS

Through large scale text mining of Twitter, our study found that there were meaningful differences in public emotions and topics regarding the COVID-19 pandemic among different UK cities. Furthermore, efficient location-based and time-based comparative analysis can be used to track people's thoughts, feelings and understand their behaviours. Based on our analysis, positive attitudes were common during the pandemic; optimism and anticipation were the dominant emotions. With the outbreak epidemiological change, the government developed control measures, vaccination policies and the topics also shifted over time. Overall, the proportion and expressions of emojis, sentiments, emotions and topics varied geographically and temporally. Therefore, our approach of exploring public emotions and topics on the pandemic from Twitter can potentially lead to informing how public policies are received in a particular geographical area.

Tweet topics and sentiments relating to distance learning among Italian Twitter users

The outbreak of COVID-19 forced a dramatic shift in education, from in-person learning to an increased use of distance learning over the past 2 years. Opinions and sentiments regarding this switch from traditional to remote classes can be tracked in real time in microblog messages promptly shared by Twitter users, who constitute a large and ever-increasing number of individuals today. Given this framework, the present study aims to investigate sentiments and topics related to distance learning in Italy from March 2020 to November 2021. A two-step sentiment analysis was performed using the VADER model and the syuzhet package to understand the overall sentiments and emotions. A dynamic latent Dirichlet allocation model (DLDA) was built to identify commonly discussed topics in tweets and their evolution over time. The results show a modest majority of negative opinions, which shifted over time until the trend reversed. Among the eight emotions of the syuzhet package, 'trust' was the most positive emotion observed in the tweets, while 'fear' and 'sadness' were the top negative emotions. Our analysis also identified three topics: (1) requests for support measures for distance learning, (2) concerns about distance learning and its application, and (3) anxiety about the government decrees introducing the red zones and the corresponding restrictions. People's attitudes changed over time. The concerns about distance learning and its future applications (topic 2) gained importance in the latter stages of 2021, while the first and third topics, which were ranked highly at first, started a steep descent in the last part of the period. The results indicate that even if current distance learning ends, the Italian people are concerned that any new emergency will bring distance learning back into use again.

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

PURPOSE

Twitter is being increasingly used by nursing professionals to share ideas, information, and opinions about the global pandemic, yet there continues to be a lack of research on how nurse sentiment is associated with major events happening on the frontline. The purpose of the study was to quantitatively identify sentiments, emotions, and trends in nurses' tweets and to explore the variations in sentiments and emotions over a period in 2020 with respect to the number of cases and deaths of COVID-19 worldwide.

DESIGN

A cross-sectional data mining study was held from March 3, 2020 through December 3, 2020. The tweets related to COVID-19 were downloaded using the tweet IDs available from a public website. Data were processed and filtered by searching for keywords related to nursing in the profile description field using the R software and JMP Pro Version 16 and the sentiment analysis of each tweet was done using AFINN, Bing, and NRC lexicon.

FINDINGS

A total of 13,868 tweets from the Twitter accounts of self-identified nurses were included in the final analysis. The sentiment scores of nurses' tweets fluctuated over time and some clear patterns emerged related to the number of COVID-19 cases and deaths. Joy decreased and sadness increased over time as the pandemic impacts increased.

CONCLUSIONS

Our study shows that Twitter data can be leveraged to study the emotions and sentiments of nurses, and the findings suggest that the emotional realm of nurses was affected during the COVID-19 pandemic according to the emotional trends observed in tweets.

CLINICAL RELEVANCE

The study provides insight into what nurses are feeling, and findings from this study highlight the importance of developing and implementing interventions targeted at nurses at the workplace to prevent mental health consequences.