On Measuring Affects of GitHub Issues' Commenters

摘要

In this study, we analyzed issues and comments on GitHub projects and built collaboration networks dividing contributors into two categories: users and commenters. We identified as commenters those users who only post comments without posting any issues nor committing changes in the source code. Since previous studies showed that there is a link between a positive environment (regarding affectiveness) and productivity, our goal was to investigate commenters' contribution to the project concerning affectiveness. We analyzed more than 370K comments from 100K issues of 25K contributors from 3 open source projects. We then calculated and compared the affectiveness of the issues' comments written by users and commenters in terms of sentiment, politeness, and emotions. We provide empirical evidence that commenters are less polite, less positive and in general they express a lower level of emotions in their comments than users. Our results also confirm that GitHub's contributors consist of different groups which behave differently, and this provides useful information for future studies in the field.