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      Impact of the Covid-19 pandemic on audiology service delivery: Observational study of the role of social media in patient communication

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          Abstract

          The Covid-19 pandemic has highlighted an era in hearing health care that necessitates a comprehensive rethinking of audiology service delivery. There has been a significant increase in the number of individuals with hearing loss who seek information online. An estimated 430 million individuals worldwide suffer from hearing loss, including 11 million in the United Kingdom. The objective of this study was to identify National Health Service (NHS) audiology service social media posts and understand how they were used to communicate service changes within audiology departments at the onset of the Covid-19 pandemic. Facebook and Twitter posts relating to audiology were extracted over a six week period (March 23 to April 30 2020) from the United Kingdom. We manually filtered the posts to remove those not directly linked to NHS audiology service communication. The extracted data was then geospatially mapped, and themes of interest were identified via a manual review. We also calculated interactions (likes, shares, comments) per post to determine the posts’ efficacy. A total of 981 Facebook and 291 Twitter posts were initially mined using our keywords, and following filtration, 174 posts related to NHS audiology change of service were included for analysis. The results were then analysed geographically, along with an assessment of the interactions and sentiment analysis within the included posts. NHS Trusts and Boards should consider incorporating and promoting social media to communicate service changes. Users would be notified of service modifications in real-time, and different modalities could be used (e.g. videos), resulting in a more efficient service.

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          Norms of online expressions of emotion: Comparing Facebook, Twitter, Instagram, and WhatsApp

          The main aim of this study was to examine the norms of expressing emotions on social media. Specifically, the perceived appropriateness (i.e. injunctive norms) of expressing six discrete emotions (i.e. sadness, anger, disappointment, worry, joy, and pride) was investigated across four different social media platforms. Drawing on data collected in March 2016 among 1201 young Dutch users (15–25 years), we found that positive expressions were generally perceived as more appropriate than negative expressions across all platforms. In line with the objective of the study, some platform differences were found. The expression of negative emotions was rated as most appropriate for WhatsApp, followed by Facebook, Twitter, and Instagram. For positive emotion expression, perceived appropriateness was highest for WhatsApp, followed by Instagram, Facebook, and Twitter. Additionally, some gender differences were found, while age showed little variations. Overall, the results contribute to a more informed understanding of emotion expression online.
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            Artificial Intelligence–Enabled Analysis of Public Attitudes on Facebook and Twitter Toward COVID-19 Vaccines in the United Kingdom and the United States: Observational Study

            Background Global efforts toward the development and deployment of a vaccine for COVID-19 are rapidly advancing. To achieve herd immunity, widespread administration of vaccines is required, which necessitates significant cooperation from the general public. As such, it is crucial that governments and public health agencies understand public sentiments toward vaccines, which can help guide educational campaigns and other targeted policy interventions. Objective The aim of this study was to develop and apply an artificial intelligence–based approach to analyze public sentiments on social media in the United Kingdom and the United States toward COVID-19 vaccines to better understand the public attitude and concerns regarding COVID-19 vaccines. Methods Over 300,000 social media posts related to COVID-19 vaccines were extracted, including 23,571 Facebook posts from the United Kingdom and 144,864 from the United States, along with 40,268 tweets from the United Kingdom and 98,385 from the United States from March 1 to November 22, 2020. We used natural language processing and deep learning–based techniques to predict average sentiments, sentiment trends, and topics of discussion. These factors were analyzed longitudinally and geospatially, and manual reading of randomly selected posts on points of interest helped identify underlying themes and validated insights from the analysis. Results Overall averaged positive, negative, and neutral sentiments were at 58%, 22%, and 17% in the United Kingdom, compared to 56%, 24%, and 18% in the United States, respectively. Public optimism over vaccine development, effectiveness, and trials as well as concerns over their safety, economic viability, and corporation control were identified. We compared our findings to those of nationwide surveys in both countries and found them to correlate broadly. Conclusions Artificial intelligence–enabled social media analysis should be considered for adoption by institutions and governments alongside surveys and other conventional methods of assessing public attitude. Such analyses could enable real-time assessment, at scale, of public confidence and trust in COVID-19 vaccines, help address the concerns of vaccine sceptics, and help develop more effective policies and communication strategies to maximize uptake.
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              “But the data is already public”: on the ethics of research in Facebook

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                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: SoftwareRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: InvestigationRole: MethodologyRole: Writing – review & editing
                Role: Writing – review & editing
                Role: Writing – review & editing
                Role: Formal analysisRole: MethodologyRole: Writing – review & editing
                Role: Formal analysisRole: Writing – review & editing
                Role: Writing – review & editing
                Role: ConceptualizationRole: SupervisionRole: Writing – review & editing
                Role: ConceptualizationRole: SupervisionRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS One
                PLoS One
                plos
                PLOS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                2024
                25 April 2024
                : 19
                : 4
                : e0288223
                Affiliations
                [1 ] School of Computing, Edinburgh Napier University, Edinburgh, Scotland
                [2 ] Edinburgh Medical School, Chancellor’s Building, The University of Edinburgh, Edinburgh, Scotland
                [3 ] Gloucestershire Hospitals NHS Trust, England, United Kingdom
                [4 ] Usher Institute, The University of Edinburgh, Edinburgh, Scotland
                Lamar University, UNITED STATES
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Author information
                https://orcid.org/0009-0003-1410-8821
                https://orcid.org/0000-0002-0559-8289
                https://orcid.org/0000-0002-7748-3296
                Article
                PONE-D-23-19309
                10.1371/journal.pone.0288223
                11045075
                38662689
                79b99460-7950-464a-9d25-070e7632545a
                © 2024 Hussain et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 21 June 2023
                : 2 March 2024
                Page count
                Figures: 3, Tables: 1, Pages: 10
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/501100000266, Engineering and Physical Sciences Research Council;
                Award ID: EP/M026981/1
                Award Recipient :
                This research is supported by the UK EPSRC COG-MHEAR programme (Grant No. 260 EP/M026981/1).
                Categories
                Research Article
                Social Sciences
                Sociology
                Communications
                Social Communication
                Social Media
                Computer and Information Sciences
                Network Analysis
                Social Networks
                Social Media
                Social Sciences
                Sociology
                Social Networks
                Social Media
                Medicine and Health Sciences
                Otorhinolaryngology
                Otology
                Audiology
                Social Sciences
                Sociology
                Communications
                Social Communication
                Social Sciences
                Sociology
                Communications
                Social Communication
                Social Media
                Twitter
                Computer and Information Sciences
                Network Analysis
                Social Networks
                Social Media
                Twitter
                Social Sciences
                Sociology
                Social Networks
                Social Media
                Twitter
                Social Sciences
                Sociology
                Communications
                Social Communication
                Social Media
                Facebook
                Computer and Information Sciences
                Network Analysis
                Social Networks
                Social Media
                Facebook
                Social Sciences
                Sociology
                Social Networks
                Social Media
                Facebook
                Medicine and Health Sciences
                Medical Conditions
                Infectious Diseases
                Viral Diseases
                Covid 19
                Medicine and Health Sciences
                Epidemiology
                Pandemics
                Medicine and Health Sciences
                Otorhinolaryngology
                Otology
                Hearing Disorders
                Deafness
                Custom metadata
                The data used in this study is now available on GitHub at [ https://github.com/kiadashtipour/Sentiment-Analysis-Adeel-Hussain].
                COVID-19

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                Uncategorized

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