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Technological advances allow new content to be created and be available via Web every minute, providing great progress in several areas. However, this availability also brings drawbacks in the Educational field. It is noteworthy that the excess of materials/content makes teaching-learning process difficult due to the high time spent in searching for content that meets the needs of the users. In this sense, new methods to identify educational content, in videos, for example, need to be developed. From this perspective, it can be seen that significant differences are identified in the comments provided by users on educational videos, thus indicating the potential for using them in the process of selecting these types of videos. In this context, the present work analyzes and collects comments from 500 videos of the Youtube platform, being 250 educational and 250 non-educational, and uses Text Mining and Machine Learning techniques to develop a classification model that, based on the most frequent words of comments on videos, categorize them as educational or non-educational. Thus, we provide a mechanism that filters videos according to their class and returns to the user only videos with educational content. Results demonstrate that it is possible to classify educational and non-educational videos with an accuracy rate of 91.30%, when using the Random Forest classifier. Furthermore, due to the promising results, we developed SysVidEduc, an API that uses the comments from Youtube videos and automatically classifies them as educational or non-educational. [ABSTRACT FROM AUTHOR]

The health-disease process encompasses factors beyond genetic and biological susceptibility, but also includes variables linked to social and economic conditions that can lead to health vulnerability. The expanding situation of COVID-19 in Brazil has demonstrated how social inequalities affect this health-disease process; thus, evaluating such disparities can help the country confront the disease. The objective of this article was to establish an index to assess the situation of social vulnerability to COVID-19. From the 12 selected variables, the modeling identified those the predicted the occurrence of COVID-19 in the State of Goiás and the Federal District. For this, two machine learning algorithms were tested: Random Forest and XGBoost. The results indicated the most predictive variables were income status, the total hospitalizations for ailments classified as very vulnerable, and the percentage of the population working informally. Therefore, approximately 23% of the municipalities were classified with high to very high vulnerability. [ABSTRACT FROM AUTHOR]