Sentiment computation of UK-originated Covid-19 vaccine Tweets: a chronological analysis and news effect.

This study aimed to analyse public sentiments of UK-originated tweets about COVID-19 vaccines using six chronological data periods between January and December 2021. The dates are based on six BBC news reports about the most significant developments in the three main vaccines administered in the UK - Pfizer-BioNTech, Moderna, and Oxford-AstraZeneca. Each data period spans seven days, starting from the news report. The study employed the Bidirectional Encoder Representations from Transformers (BERT) model to analyse the sentiments in the 4,172 extracted tweets. The BERT model adopts the transformer architecture and uses the 'Masked Language Model' and 'Next Sentence Prediction'. The results show that the overall sentiments for all three vaccines were negative across all six periods, with Moderna having the least negative tweets and the highest percentage of positive tweets overall, while AstraZeneca attracted the most negative tweets. However, for all the considered periods, period 3 (23 -29 May 2021) received the least negative and the most positive tweets, following the BBC report – COVID - Pfizer and AstraZeneca jabs work against Indian variant, despite reports of blood clot cases associated with AstraZeneca in the same period. Periods 5 to 6, where there was no breaking news relating to COVID Vaccines, had no significant changes. We, therefore, conclude that the BBC News reports on COVID Vaccines significantly impacted public sentiments regarding the COVID-19 Vaccines

Sentiment computation of UK-originated COVID-19 vaccine tweets: a chronological analysis and news effect

This study aimed to analyse public sentiments of UK-originated tweets related to COVID-19 vaccines, and it applied six chronological time periods, between January and December 2021. The dates were related to six BBC news reports about the most significant developments in the three main vaccines that were being administered in the UK at the time: Pfizer-BioNTech, Moderna, and Oxford-AstraZeneca. Each time period spanned seven days, starting from the day of the news report. The study employed the bidirectional encoder representations from transformers (BERT) model to analyse the sentiments in 4172 extracted tweets. The BERT model adopts the transformer architecture and uses masked language and next sentence prediction models. The results showed that the overall sentiments for all three vaccines were negative across all six periods, with Moderna having the least negative tweets and the highest percentage of positive tweets overall while AstraZeneca attracted the most negative tweets. However, for all the considered time periods, Period 3 (23–29 May 2021) received the least negative and the most positive tweets, following the related BBC report—’COVID: Pfizer and AstraZeneca jabs work against Indian variant’—despite reports of blood clots associated with AstraZeneca during the same time period. Time periods 5 and 6 had no breaking news related to COVID vaccines, and they reflected no significant changes. We, therefore, concluded that the BBC news reports on COVID vaccines significantly impacted public sentiments regarding the COVID-19 vaccines

An Analysis of COVID-19 Vaccine Hesitancy in the U.S. at the County Level

Reluctance or refusal to get vaccinated, referred to as vaccine hesitancy (VH), has hindered the efforts of COVID-19 vaccination campaigns. It is important to understand what factors impact VH behavior. This information can help design public health interventions that could potentially increase vaccine uptake. We develop a random forest (RF) classification model that uses a wide variety of data to determine what factors affected VH at the county level during 2021. We consider static factors (such as gender, race, political affiliation, etc.) and dynamic factors (such as Google searches, social media postings, Stringency Index, etc.). Our model found political affiliation and the number of Google searches to be the most relevant factors in determining VH behavior. The RF classification model grouped counties of the U.S. into 5 clusters. VH is lowest in cluster 1 and highest in cluster 5. Most of the people who live in cluster 1 are democrat, are more internet-inquisitive (are more prone to seek information from multiple sources on the internet), have the longest life expectancy, have a college degree, have the highest income per capita, live in metropolitan areas. Most people who live in cluster 5 are republicans, are the least internet-inquisitive, have the shortest life expectancy, do not have a college degree, have the lowest income per capita, and live in non-metropolitan areas. Our model found that counties in cluster 1 were most responsive to vaccination-related policies and COVID-19 restrictions. These strategies did not have an impact on the VH of counties in cluster 5.Comment: 28 pages, 12 figures, 4 table

Desinformación y vacunas en redes Comportamiento de los bulos en Twitter

Introducción: La desinformación antivacunas tiene un gran peligro por sus efectos tangibles en la sociedad. Existen investigaciones relevantes sobre tipologías de bulos, discursos negacionistas en redes o la popularidad de las vacunas, pero este estudio aporta una visión complementaria y pionera sobre el discurso antivacunas de COVID-19 en Twitter, centrada en el comportamiento de sus propagadores. Metodología: Dada una muestra inicial de un centenar de bulos (de diciembre de 2020 a septiembre de 2021) para la descarga de 200.246 tuits, se han filtrado mediante un algoritmo para la inferencia del lenguaje natural (NLI) alrededor de 36.000 tuits (N=36.292) que apoyan o desmienten la desinformación para analizar a sus difusores a través de sus métricas en la plataforma. Resultados: En números relativos, los resultados muestran, entre otros, más bulos con contenido original (no retuits) entre las cuentas con más seguidores y aquellas verificadas; más irrupción de desinformación frente a su objeción por cuentas creadas de 2013 a 2020, y la asociación del reconocimiento (mayor presencia en listas o muchos más seguidores que seguidos) a la preferencia por negar información falsa en lugar de aprobarla. Discusión: El artículo muestra cómo la tipología de las cuentas es un factor predictivo del comportamiento de usuarios que expanden desinformación. Conclusiones: Se revelan patrones similares de comportamiento del discurso antivacunas según indicadores de las cuentas de Twitter. El tamaño de la muestra y las técnicas empleadas dan una base sólida para otros estudios comparativos en desinformación sobre salud y en otros fenómenos en redes sociales.Ciencias de la Comunicació

Disinformation and vaccines on social networks: Behavior of hoaxes on Twitter

Anti-vaccine disinformation is highly dangerous due to its direct effects on society. Although there is relevant research on typologies of hoaxes, denialist discourses on networks, or the popularity of vaccines, this study provides a complementary and pioneering vision about the antivaccine discourse of COVID-19 on Twitter, focused on its spreaders’ behavior. Methodology: Given an initial sample of a hundred hoaxes (from December 2020 to September 2021) for the download of 200,246 tweets, around 36,000 tweets (N=36.292) that support or deny disinformation have been filtered through an algorithm for Natural Language Inference (NLI) to analyze their spreaders’ through their metrics in the platform. Results: In relative numbers, the results show, among others, more hoaxes with original content (not retweets) among accounts with more followers and those verified; more irruption of disinformation as opposed to its objection by accounts created between 2013 and 2020, and the association of the acknowledgment (more presence in lists or many more followers than followed users) to the preference for denying false information instead of approving it. Discussion: The article shows how the typology of the accounts can be a predictive factor about the behavior of users who spread disinformation. Conclusions: Similar behavioral patterns of anti-vaccine discourse are revealed according to the accounts’ Twitter-related indicators. The size of the sample and the techniques used give a solid foundation for other comparative studies on disinformation about health and other phenomena on social networks.Ciencias de la Comunicació

Análise de informações divulgadas em mídia social acerca do sarampo e sua vacina

Trabalho de Conclusão de Curso (graduação)—Universidade de Brasília, Faculdade de Ceilândia, 2019.Introdução: Recentemente o Brasil perdeu o certificado de país livre do sarampo, há indícios que isso tenha ocorrido tanto por processos migratórios, como por redução da cobertura vacinal, o que pode ter influência de mídias sociais. Objetivo: Analisar as informações divulgadas por perfis jornalísticos e institucionais na mídia social Twitter® referente ao sarampo e à sua vacina. Métodos: trata-se de um estudo exploratório com caráter descritivo de análise de conteúdos relacionados ao Sarampo e Vacinas contra o Sarampo publicadas no Twitter® entre os meses de janeiro a agosto de 2019. Resultados: Foram analisados 401 tweets relacionados ao Sarampo e sua vacina. Obteve-se uma média de 36,6 (± 59,2) curtidas, 12,0 (±25,8) retweets e 1,5 (±3,5) comentários por tweet. Dos 401 tweets, o Ministério da Saúde teve maior frequência de publicação com 105 (26,2%) e o G1 obteve maiores médias de curtidas (132,7±144,3), retweets (36,0±56,8) e comentários (6,92±9,0) por tweet. A maior parte dos tweets (92,0%) apresentava informações baseadas em evidências. Conclusão: Os achados apontam que se faz necessário métodos e estratégias de engajamento, a fim de alcançar melhores resultados de interação com os usuários da plataforma para que as informações possam ser mais efetivas e compartilhadas.Introduction: Brazil has recently lost its measles-free country certificate. There are indications that this has been due to both migratory processes and reduced immunization coverage, which may be influenced by social media. Objective: To analyze information disseminated by journalistic and institutional profiles on Twitter® social media regarding measles and its vaccine. Methods: This is a descriptive exploratory study of content analysis related to Measles and Measles Vaccines published on Twitter® between January and August 2019. Results: We analyzed 401 tweets related to Measles and its vaccine. We got 36.6 (± 59.2) likes, 12.0 (±25.8) retweets, and 1.5 (±3.5) comments per tweet. Of the 401 tweets, the Ministry of Health had the highest publication frequency with 105 (26.2%) and G1 had the highest likes (132.7 ± 144.3), retweets (36.0 ± 56.8) and comments (6.92 ± 9.0) by tweet. Most tweets (92.0%) had evidencebased information. Conclusion: The findings indicate that engagement methods and strategies are needed in order to achieve better results of interaction with platform users so that information can be more productive and shared

Facebook and Twitter vaccine sentiment in response to measles outbreaks

Social media posts regarding measles vaccination were classified as pro-vaccination, expressing vaccine hesitancy, uncertain, or irrelevant. Spearman correlations with Centers for Disease Control and Prevention-reported measles cases and differenced smoothed cumulative case counts over this period were reported (using time series bootstrap confidence intervals). A total of 58,078 Facebook posts and 82,993 tweets were identified from 4 January 2009 to 27 August 2016. Pro-vaccination posts were correlated with the US weekly reported cases (Facebook: Spearman correlation 0.22 (95% confidence interval: 0.09 to 0.34), Twitter: 0.21 (95% confidence interval: 0.06 to 0.34)). Vaccine-hesitant posts, however, were uncorrelated with measles cases in the United States (Facebook: 0.01 (95% confidence interval: -0.13 to 0.14), Twitter: 0.0011 (95% confidence interval: -0.12 to 0.12)). These findings may result from more consistent social media engagement by individuals expressing vaccine hesitancy, contrasted with media- or event-driven episodic interest on the part of individuals favoring current policy

Understanding misinformation on Twitter in the context of controversial issues

Social media is slowly supplementing, or even replacing, traditional media outlets such as television, newspapers, and radio. However, social media presents some drawbacks when it comes to circulating information. These drawbacks include spreading false information, rumors, and fake news. At least three main factors create these drawbacks: The filter bubble effect, misinformation, and information overload. These factors make gathering accurate and credible information online very challenging, which in turn may affect public trust in online information. These issues are even more challenging when the issue under discussion is a controversial topic. In this thesis, four main controversial topics are studied, each of which comes from a different domain. This variation of domains can give a broad view of how misinformation is manifested in social media, and how it is manifested differently in different domains. This thesis aims to understand misinformation in the context of controversial issue discussions. This can be done through understanding how misinformation is manifested in social media as well as by understanding people’s opinions towards these controversial issues. In this thesis, three different aspects of a tweet are studied. These aspects are 1) the user sharing the information, 2) the information source shared, and 3) whether specific linguistic cues can help in assessing the credibility of information on social media. Finally, the web application tool TweetChecker is used to allow online users to have a more in-depth understanding of the discussions about five different controversial health issues. The results and recommendations of this study can be used to build solutions for the problem of trustworthiness of user-generated content on different social media platforms, especially for controversial issues