This article examined, through Social Network Analysis (SNA) techniques, the personal profiles of theHeads of Government of countries in South and North America and how they communicated withtheir audiences on institutional measures to contain COVID-19. Analyses were carried out on data collected from Twitter from November 2019 to November 2020. This study includes: i) quantitative analysis, measuring categories and emphases in the communication of tweets, retweets, likes, and comments on matters relevant to the pandemic; ii) qualitative analysis that allowed evaluating speeches to identify political interference and the effectiveness of communication at critical moments of the pandemic. It was possible to infer that each president has his singularities and understanding about Social Media's use as a more direct communication tool with his audience. It was also found that successful communication is not directly proportional to the volume of messages on Twitter, but to socio-political aspects and institutional leadership that can make a difference in Social Media in combating COVID-19. I. INTRODUCTIONThe world is experiencing a challenge of grand proportions, the discovery and a worldwide spread of the Covid-19 virus at the end of 2019 and throughout 2020. This occurrence showed the global fragility of the Health Systems and a communicational difficulty in the face of Disinformation. Social Media has become an open pathway for a flood of fake news propagated mainly on social media. In addition to competing with misinformation on social media, governments deal with an internal difficulty to create disclosure processes consistent with the line of action in fighting the Pandemic. SARS-CoV-2 and COVID-19 disease provide an opportunity to investigate the consequences of functional fragmentation in government communication when the issues concern public health and pandemic response (ZEEMERING, 2020).According to United Nations Educational, Scientific and Cultural Organization (UNESCO, 2019), finding ways through contemporary information challenges is of utmost importance for society, including governments, Internet companies, educators and NGOs.

The evaluation of personality traits allows the study of human behavior in different environments, but it is not a trivial task. In this sense, the Five-Factor Model (FFM) allows, in a global way, the assessment of personality traits of individuals using textual data. However, there is a scarcity of lexical resources for languages other than English, which generated the main research question of this work: "Can models trained to predict FFM personality traits using English textual data show satisfactory results when applied to textual data in other languages?". Therefore, this work aims to answer: (i) Whether Word Embeddings techniques could be used to solve low resources languages problems in FFM personality traits prediction; and (ii) Whether is feasible to train a traditional Machine Learning algorithm with English language textual data and evaluate its performance with Brazilian Portuguese language textual data for FFM personality traits prediction. Thus, the work aims to present an approach in which the models can be used to learn the highest level of abstraction. As results, we observed that the difference in performance between the models trained for personality recognition in English is minimal when used to predict FFM personality traits in Brazilian Portuguese texts. In this task, the Stochastic Gradient Descent model presents the best average results among the FFM personality traits of the models analyzed.

This article examined the personal profiles of the Heads of Government of countries in South/North America and how they communicated with their audiences on institutional measures to contain COVID-19. Analyses were carried out on data collected from Twitter from November-2019 to November-2020. This study includes: i)quantitative analysis, measuring categories and emphases in the communication of tweets, retweets, likes, and comments on matters relevant to the pandemic; ii)qualitative analysis that allowed evaluating speeches to identify political interference and the effectiveness of communication at critical moments of the pandemic. It was possible to infer that each president has his singularities and understanding about Social Media’s use as a more direct communication tool with his audience. It was also found that successful communication is not directly proportional to the volume of messages on Twitter, but to socio-political aspects and institutional leadership that can make a difference in Social Media in combating COVID-19.

No abstract

Desde o início de 2020 o mundo vive uma crise de saúde ocasionada pela COVID-19. Embora a pandemia seja devastadora em todo o mundo, as ações de enfrentamento e os impactos sofridos são distintos entre as nações. No entanto, a vacina é uma das principais ferramentas para o controle da pandemia. Neste cenário, as Redes Sociais Online (RSO) se tornaram um espaço significativo para atividade cívica e política, estando entre as fontes de informação mais utilizadas no mundo. Este artigo visa reportar uma análise das publicações sobre vacinas contra a COVID-19 de usuários brasileiros e do presidente do Brasil na plataforma Twitter. Técnicas de Aprendizado de Máquina (Machine Learning) foram utilizadas e os resultados mostram que o modelo Support Vector Machine foi o que conseguiu melhor desempenho com 60,72% de acurácia com extração de parâmetro ReliefF para a análise dos tweets que indicavam quais as vacinas mais mencionadas nos perfis do presidente e dos usuários.