ANÁLISE DE DADOS A PARTIR DE COMENTÁRIOS EM VÍDEOS DE ENSINO DE PROGRAMAÇÃO NO YOUTUBE

Abstract

In the current scenario, marked by the increasing advance of technology, social paradigms have adapted by incorporating the use of digital platforms into everyday contexts. In the educational context, the use of platforms such as YouTube as a tool for sharing and disseminating content has become popular in various areas, including the field of programming education, generating a framework of data both in terms of content and user interactions stemming from that content. However, the volume of data generated through the interaction between videos and students makes manual analysis of these data impractical. This study aims to analyze comments on programming education videos published on the YouTube platform based on a gender perspective, with the goal of identifying patterns and discrepancies among the data. Three experiments were conducted: one focused on quantitative aspects of comments, such as the average number of characters and the average number of questions; the second focused on qualitative aspects, establishing a sentiment analysis in video comments and searching for subjective aspects present in the comments. The experiments showed minimal differences in the quantitative part for the two classes selected for the research. The video data exhibited a neutral correlation, not allowing any conclusions to be drawn from these results. In the quantitative analysis of comments, both groups showed similar averages and medians for the number of characters per comment, but the standard deviation and the maximum showed that comments with a higher number of characters are in videos featuring men. These videos also had the highest number of comments that were questions. The sentiment analysis showed a higher percentage of "positive" comments in women's videos compared to comments classified as "negative" or "neutral." However, qualitative analysis highlighted aspects of delegitimization and stereotyping in comments on women's videos, showing the occurrence of words like "beautiful" and "attractive" that are not present in comments on men's videos.