Contribuição da computação na análise de dados de redes sociais no contexto da pandemia da COVID-19

Abstract

Social Networks are platforms that allow connectivity and interaction of different people and cultures, generating thousands of data from this interaction. Social Network Data Analysis allows such data to be analyzed in order to obtain relevant information about what is being discussed or how people are feeling in a given context. The course conclusion work presented here has the general objective of carrying out a Systematic Literature Review on the contribution of computing to the Analysis of Social Network Data, especially in the context of the COVID-19 pandemic. Obtaining an overview of the methods and strategies produced by computing to analyze data from social networks in this context can help researchers in the field to deal in the best possible way in relation to possible public health emergencies that may arise. The proposed methodology for this work consisted of carrying out a Mixed Sequential Exploratory Review that consisted of two phases, Quantitative Analysis and Qualitative Analysis. A total of 784 articles were collected and after applying the inclusion and exclusion criteria, 140 articles were selected. By analyzing the selected articles, it was possible to identify the main techniques, methods and tools used in data analysis. In addition, it was also possible to observe which categories of methods were most explored, how the distribution of articles is being, which countries contributed, how computing contributed to different areas of science, which were the most explored tools and what was the period of collection of data from the analyzed articles.