In the context of COVID-19 pandemic, social networks such as Facebook, Twitter, YouTube and Instagram stand out as important sources of information. Among those, YouTube, as the largest and most engaging online media consumption platform, has a large influence in the spread of information and misinformation, which makes it important to study how the platform deals with the problems that arise from disinformation, as well as how its users interact with different types of content. Considering that United States (USA) and Brazil (BR) are two countries with the highest COVID-19 death tolls, we asked the following question: What are the nuances of vaccination campaigns in the two countries? With that in mind, we engage in a comparative analysis of pro and anti-vaccine movements on YouTube. We also investigate the role of YouTube in countering online vaccine misinformation in USA and BR. For this means, we monitored the removal of vaccine related content on the platform and also applied various techniques to analyze the differences in discourse and engagement in pro and anti-vaccine "comment sections". We found that American anti-vaccine content tend to lead to considerably more toxic and negative discussion than their pro-vaccine counterparts while also leading to 18% higher user-user engagement, while Brazilian anti-vaccine content was significantly less engaging. We also found that pro-vaccine and anti-vaccine discourses are considerably different as the former is associated with conspiracy theories (e.g. ccp), misinformation and alternative medicine (e.g. hydroxychloroquine), while the latter is associated with protective measures. Finally, it was observed that YouTube content removals are still insufficient, with only approximately 16% of the anti-vaccine content being removed by the end of the studied period, with the United States registering the highest percentage of removed anti-vaccine content(34%) and Brazil registering the lowest(9.8%).CCS Concepts: • Human-centered computing → Empirical studies in collaborative and social computing.

COVID-19 rapidly spread across the world in an unprecedented outbreak with a massive number of infected and fatalities. The pandemic was heavily discussed and searched on the internet, which generated big amounts of data related to it. This led to the possibility of attempting to forecast coronavirus indicators using the internet data. For this study, Google Trends statistics for 124 selected search terms related to pandemic were used in an attempt to find which keywords had the best Spearman correlations with a lag, as well as a forecasting model. It was found that keywords related to coronavirus testing among some others, such as "I have contracted covid", had high correlations (≥0.7) with few weeks of lag (≤4 weeks). Besides that, the ARIMAX model using those keywords had promising results in predicting the increase or decrease of epidemiological indicators, although it was not able to predict their exact values. Thus, we found that Google Trends data may be useful for predicting outbreaks of coronavirus a few weeks before they happen, and may be used as an auxiliary tool in monitoring and forecasting the disease in Brazil.

O geoprocessamento de dados e as análises espaciais são ferramentas importantes para o estudo de fenômenos como a disseminação de doenças pelo território e ao longo do tempo. O objetivo deste estudo é investigar, utilizando a Análise Exploratória de Dados Espaciais (AEDE), as alterações nos padrões de distribuição geográfica da Covid-19 no Brasil em dois períodos distintos da pandemia: (i) entre abril e agosto de 2020; e (ii) entre novembro de 2020 e março de 2021. Para tanto, as estatísticas I de Moran e LISA foram aplicadas aos dados referentes a três indicadores epidemiológicos: casos acumulados, novos casos e letalidade da doença. Os resultados encontrados e as visualizações propostas apresentam uma perspectiva ampla sobre a variação nos casos de Covid-19 nas regiões brasileiras e colaboram para um melhor entendimento sobre as dinâmicas epidemiológicas no Brasil no primeiro ano da pandemia de Covid-19.

O presente estudo busca caracterizar o primeiro ano da pandemia de COVID-19 no Brasil como um fenômeno social por meio da análise da correlação entre o agravamento/atenuação da pandemia e o vocabulário utilizado no Twitter nas semanas que precedem essas variações. Entre outros resultados, observou-se que termos politicamente motivados e com teor negativo são mais prevalentes nas semanas que precedem o aumento do número de casos/mortes, ao passo que o uso de termos relacionados a conteúdos midiáticos (internet, música, televisão) é intensificado nas semanas que antecedem a queda da quantidade de casos/mortes. Tais resultados sugerem a possibilidade de utilização do método aqui introduzido para a análise de fenômenos sociais a partir de dados computacionalmente leves e totalmente anonimizados provenientes de redes sociais online.