RESUMO
The context of the COVID-19 pandemic has brought to light the infodemic phenomenon and the problem of misinformation. Agencies involved in managing COVID-19 immunization programs are also looking for ways to combat this problem, demanding analytical tools specialized in identifying patterns of misinformation and understanding how they have evolved in time and space to demonstrate their effects on public trust. The aim of this article is to present the results of a study applying topic analysis in space and time with respect to public opinion on the Brazilian COVID-19 immunization program. The analytical process involves applying topic discovery to tweets with geoinformation extracted from the COVID-19 vaccination theme. After extracting the topics, they were submitted to manual annotation, whereby the polarity labels pro, anti, and neutral were applied based on the support and trust in the COVID-19 vaccination. A space and time analysis was carried out using the topic and polarity distributions, making it possible to understand moments during which the most significant quantities of posts occurred and the cities that generated the most tweets. The analytical process describes a framework capable of meeting the needs of agencies for tools, providing indications of how misinformation has evolved and where its dissemination focuses, in addition to defining the granularity of this information according to what managers define as adequate. The following research outcomes can be highlighted. (1) We identified a specific date containing a peak that stands out among the other dates, indicating an event that mobilized public opinion about COVID-19 vaccination. (2) We extracted 23 topics, enabling the manual polarity annotation of each topic and an understanding of which polarities were associated with tweets. (3) Based on the association between polarities, topics, and tweets, it was possible to identify the Brazilian cities that produced the majority of tweets for each polarity and the amount distribution of tweets relative to cities populations.
RESUMO
This article presents a study that applied opinion analysis about COVID-19 immunization in Brazil. An initial set of 143,615 tweets was collected containing 49,477 pro- and 44,643 anti-vaccination and 49,495 neutral posts. Supervised classifiers (multinomial naïve Bayes, logistic regression, linear support vector machines, random forests, adaptative boosting, and multilayer perceptron) were tested, and multinomial naïve Bayes, which had the best trade-off between overfitting and correctness, was selected to classify a second set containing 221,884 unclassified tweets. A timeline with the classified tweets was constructed, helping to identify dates with peaks in each polarity and search for events that may have caused the peaks, providing methodological assistance in combating sources of misinformation linked to the spread of anti-vaccination opinion.
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The Bedouin syndrome represents social interactions based on four premises: a friend of my friend is my friend, a friend of my enemy is my enemy, an enemy of my friend is my enemy, and an enemy of my enemy is my friend. These extensive associations exist in many social and economic relationships, such as market competition, neighborhood relations, political behavior, student gangs, organized crime, and the violent behavior of sports spectators (hooliganism) worldwide. This work tests the Bedouin syndrome hypothesis considering the violent behavior in the football fan culture. We construct relational networks of social affinities to represent the social interactions of organized fan bases (Torcidas organizadas) involved in hooligan violence in Pernambuco, Brazil. Contrary to prior expectations, the results evidence no statistical support for the Bedouin syndrome in 13 of the 15 analyzed clubs. There is weak statistical support in two interactions and strong statistical support in one interaction to state that a friend of my enemy is my friend (instead of an enemy). The only support for the Bedouin syndrome is circumstantial based on a prior assumption of an alliance. We propose a network development that can be more suitable to represent football fans' violent behavior. The results contribute to understanding the hooliganism social phenomenon in football-rooted cultures and their impact on public health, identifying potential determinants for organized violence by young spectators' and supporting police strategies by defining relevance scores for the most potential clashes and coalitions of gangs.
Assuntos
Futebol Americano , Futebol , Agressão , Árabes , Humanos , ViolênciaRESUMO
Hospital organizations have adopted telehealth systems to expand their services to a portion of the Brazilian population with limited access to healthcare, mainly due to the geographical distance between their communities and hospitals. The importance and usage of those services have recently increased due to the COVID-19 state-level mobility interventions. These services work with sensitive and confidential data that contain medical records, medication prescriptions, and results of diagnostic processes. Understanding how cybersecurity impacts the development of telehealth strategies is crucial for creating secure systems for daily operations. In the application reported in this article, the Fuzzy Cognitive Maps (FCMs) translated the complexity of cybersecurity in telehealth services into intelligible and objective results in an expert-based cognitive map. The tool also allowed the construction of scenarios simulating the possible implications caused by common factors that affect telehealth systems. FCMs provide a better understanding of cybersecurity strategies using expert knowledge and scenario analysis, enabling the maturation of cybersecurity in telehealth services.
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The purpose of this paper is to propose a framework for cybersecurity risk management in telemedicine. The framework, which uses a bow-tie approach for medical image diagnosis sharing, allows the identification, analysis, and assessment of risks, considering the ISO/TS 13131:2014 recommendations. The bow-tie method combines fault tree analysis (FTA) and event tree analysis (ETA). The literature review supported the identification of the main causes and forms of control associated with cybersecurity risks in telemedicine. The main finding of this paper is that it is possible, through a structured model, to manage risks and avoid losses for everyone involved in the process of exchanging medical image information through telemedicine services. Through the framework, those responsible for the telemedicine services can identify potential risks in cybersecurity and act preventively, recognizing the causes even as, in a mitigating way, identifying viable controls and prioritizing investments. Despite the existence of many studies on cybersecurity, the paper provides theoretical contributions to studies on cybersecurity risks and features a new methodological approach, which incorporates both causes and consequences of the incident scenario.