Vaccination Talks on Twitter. Semantic Social Networks and Public Views From Greece (original) (raw)
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A Social Network Analysis of Tweets Related to Mandatory COVID-19 Vaccination in Poland
Vaccines 2022, 10, 750., 2022
Poland’s efforts to combat COVID-19 were hindered by endemic vaccination hesitancy and the prevalence of opponents to pandemic restrictions. In this environment, the policy of a COVID-19 vaccination mandate faces strong resistance in the public debate. Exploring the discourse around this resistance could help uncover the motives and develop an understanding of vaccination hesitancy in Poland. This paper aims to conduct a social network analysis and content analysis of Twitter discussions around the intention of the Polish Ministry of Health to introduce mandatory vaccinations for COVID-19. Twitter was chosen as a platform to study because of the critical role it played during the global health crisis. Twitter data were retrieved from 26 July to 9 December 2021 through the API v2 for Academic Research, and analysed using NodeXL and Gephi. When conducting social network analysis, nodes were ranked by their betweenness centrality. Clustering analysis with the Clauset–Newman–Moore algorithm revealed two important groups of users: advocates and opponents of mandatory vaccination. The temporal trends of tweets, the most used hashtags, the sentiment expressed in the most popular tweets, and correlations with epidemiological data were also studied. The results reveal a substantial degree of polarisation, a high intensity of the discussion, and a high degree of involvement of Twitter users. Vaccination mandate advocates were consistently more numerous, but less engaged and less mobilised to “preach” their own stances. Vaccination mandate opponents were vocal and more mobilised to participate: either as original authors or as information diffusers. Our research leads to the conclusion that systematic monitoring of the public debate on vaccines is essential not only in counteracting misinformation, but also in crafting evidence-based as well as emotionally motivating narratives.
A Content and Sentiment Analysis of Greek Tweets during the Pandemic
Sustainability
During the time of the coronavirus, strict prevention policies, social distancing, and limited contact with others were enforced in Greece. As a result, Twitter and other social media became an important place of interaction, and conversation became online. The aim of this study is to examine Twitter discussions around COVID-19 in Greece. Twitter was chosen because of the critical role it played during the global health crisis. Tweets were recorded over four time periods. NodeXL Pro was used to identify word pairs, create semantic networks, and analyze them. A lexicon-based sentiment analysis was also performed. The main topics of conversation were extracted. “New cases” are heavily discussed throughout, showing fear of transmission of the virus in the community. Mood analysis showed fluctuations in mood over time. Positive emotions weakened and negative emotions increased. Fear is the dominant sentiment. Timely knowledge of people’s sentiment can be valuable for government agencies...
Asia Pacific Journal of Multidisciplinary Research, 2020
Several researchers have presented several studies on the CoViD-19 outbreak like on the epidemiological aspects of the disease, diagnostics method of the novel coronavirus, clinical characteristics, transmission, and vaccines. However, the sentiments and behaviour of the people online particularly in twitter remain unexplored. In this paper we focused on exploring peoples’ tweets to uncover their attitudes, sentiments, and find out the network effects of peoples’ tweets and the heated topics.Text mining approach was utilized using sentiment and social network analysis. Term document matrix, word cloud, nrc_sentiment dictionary, histogram, community edge betweenness algorithm, and network graph were used in the study. An API account was created wherein15000 tweets were extracted from March 22, 2020 to March 31, 2020 containing the keyword #COVID-19 to make a working data for analysis. Results from the social network analysis showed a close relationship between tweets where people are globally talking part by sharing information about the CoViD-19. The peoples’ attitude showed the willingness to follow government precautionary measures to lessen the impact of the virus. Despite of the fear and sadness felt by the people over twitter, sentiment analysis revealed positive emotion towards the crisis. Such insights are significant when guiding people to respond appropriately and helping them to learn to cope with the sudden infectious disease as it promotes social stability. This will also help the authorities understand the sentiments and anxieties of the people, giving a strong direction to enact policies beneficial to the people. Moreover, social network analysis can be used as a method of understanding the behaviour of the people online and how these people are talking towards an issue.
ArXiv, 2021
Background: Measuring what and how information generates and propagates in online communities such as Twitter user communities make a distinguishable characteristic over the of demographical region and it helps the public health organization to take appropriate decision to develop the public health system. Objective: We formed community structure based on web page credibility and we measured the types of information for characterizing communities of tweeter users who posted about tweets related to vaccine. Methods: We performed the experiment on only Twitter data (tweets) regarding vaccine. The duration of data collection was between 17 January 2017 and 14 March 2018. We formulated cluster based on the information on its contents and sources it resides (i.e., website domains). We only focused the topics which were related to vaccine. To detect the structure and network of community, we applied Louvain community algorithm along with Random walks called Info map method over vaccines r...
Human Vaccines & Immunotherapeutics, 2022
BACKGROUND/AIM The first case of COVID-19 in Turkey was officially recorded on March 11, 2020. Social media use increased worldwide, as well as in Turkey, during the pandemic, and conspiracy theories/fake news about medical complications of vaccines spread throughout the world. The aim of this study was to identify community interactions related to vaccines and to identify key influences/influencers before and after the pandemic using social network data from Twitter. MATERIALS AND METHODS Two datasets, including tweets about vaccinations before and after COVID-19 in Turkey, were collected. Social networks were created based on interactions (mentions) between Twitter users. Users and their influence were scored based on social network analysis and parameters that included in-degree and betweenness centrality. RESULTS In the pre-COVID-19 network, media figures and authors who had anti-vaccine views were the most influential users. In the post-COVID-19 network, the Turkish minister of health, the was the most influential figure. The vaccine network was observed to be growing rapidly after COVID-19, and the physicians and authors who had opinions about mandatory vaccinations received a great deal of reaction. One-way communication between influencers and other users in the network was determined. CONCLUSIONS This study shows the effectiveness and usefulness of large social media data for understanding public opinion on public health and vaccination in Turkey. The current study was completed before the implementation of the COVID-19 vaccine in Turkey. We anticipated that social network analysis would help reduce the "infodemic" before administering the vaccine and would also help public health workers act more proactively in this regard.
Twitter Network Sentimental Analysis on Vaccination
International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2022
Globally, the COVID-19 pandemic has had an impact on daily life. Since the start of the pandemic, numerous research teams at significant pharmaceutical corporations and academic institutions throughout the world have been creating vaccines. Gender has an effect on vaccine responses, acceptability, and results. Additionally, the global advertising of the COVID-19 vaccine sparks a lot of conversations on social media outlets regarding the protection and effectiveness of vaccines, among other things. Twitter is viewed as one of the most popular social media sites that has been extensively used to communicate the public's thoughts on issues with the COVID-19 pandemic vaccination. However, there hasn't been enough research done to examine the analysis of the general public's view of the COVID-19 vaccine from a feminist perspective. The COVID-19 pandemic has been widely covered in social media, conventional print media, and electronic media since it first surfaced in December 2019. These sites provide data from reliable and unreliable medical sources. Additionally, the news from these mediums disseminates quickly. Spreading false information can cause anxiety, unintended exposure to medical treatments, digital marketing scams, and even lethal consequences. Therefore, it is imperative to develop a model for identifying bogus news in the news pool. The dataset employed in this work, which combines news about COVID-19 from various social media and news sources, is used for categorization.
Social Network Analysis of COVID-19 Public Discourse on Twitter: Implications for Risk Communication
Disaster Medicine and Public Health Preparedness, 2020
Objectives: The purpose of this study was to demonstrate the use of social network analysis to understand public discourse on Twitter around the novel coronavirus disease 2019 (COVID-19) pandemic. We examined different network properties that might affect the successful dissemination by and adoption of public health messages from public health officials and health agencies. Methods: We focused on conversations on Twitter during 3 key communication events from late January to early June of 2020. We used Netlytic, a Web-based software that collects publicly available data from social media sites such as Twitter. Results: We found that the network of conversations around COVID-19 is highly decentralized, fragmented, and loosely connected; these characteristics can hinder the successful dissemination of public health messages in a network. Competing conversations and misinformation can hamper risk communication efforts in a way that imperil public health. Conclusions: Looking at basic m...
Journal of Medical Internet Research, 2015
Background: In May 2013, a measles outbreak began in the Netherlands among Orthodox Protestants who often refuse vaccination for religious reasons. Objective: Our aim was to compare the number of messages expressed on Twitter and other social media during the measles outbreak with the number of online news articles and the number of reported measles cases to answer the question if and when social media reflect public opinion patterns versus disease patterns. Methods: We analyzed measles-related tweets, other social media messages, and online newspaper articles over a 7-month period (April 15 to November 11, 2013) with regard to topic and sentiment. Thematic analysis was used to structure and analyze the topics. Results: There was a stronger correlation between the weekly number of social media messages and the weekly number of online news articles (P<.001 for both tweets and other social media messages) than between the weekly number of social media messages and the weekly number of reported measles cases (P=.003 and P=.048 for tweets and other social media messages, respectively), especially after the summer break. All data sources showed 3 large peaks, possibly triggered by announcements about the measles outbreak by the Dutch National Institute for Public Health and the Environment and statements made by well-known politicians. Most messages informed the public about the measles outbreak (ie, about the number of measles cases) (93/165, 56.4%) followed by messages about preventive measures taken to control the measles spread (47/132, 35.6%). The leading opinion expressed was frustration regarding people who do not vaccinate because of religious reasons (42/88, 48%). Conclusions: The monitoring of online (social) media might be useful for improving communication policies aiming to preserve vaccination acceptability among the general public. Data extracted from online (social) media provide insight into the opinions that are at a certain moment salient among the public, which enables public health institutes to respond immediately and appropriately to those public concerns. More research is required to develop an automatic coding system that captures content and user's characteristics that are most relevant to the diseases within the National Immunization Program and related public health events and can inform official responses.
COVID-19 and Public Health: Analysis of Opinions in Social Media
International Journal of Environmental Research and Public Health
The article presents the results of research of public opinion during the third wave of the COVID-19 pandemic in Russia. The study touches on the attitude of citizens to public health, as well as the reaction of social media users to government measures in a crisis situation during a pandemic. Special attention is paid to the phenomenon of infodemic and methods of detecting cases of the spread of false and unverified information about diseases. The article demonstrates the application of an interdisciplinary approach using network analysis of texts and sociological research. A model for detecting social stress in the textual communication of social network users using a specially trained neural network and linguistic analysis methods is presented. The validity and validity of the results of the analysis of social network data were verified using a sociological survey. This approach allows us to identify points of tension in matters of public health promotion, during crisis situation...