Selecting an appropriate task is challenging for newcomers to Open Source Software (OSS) projects. To facilitate task selection, researchers and OSS projects have leveraged machine learning techniques, historical information, and textual analysis to label tasks (a.k.a. issues) with information such as the issue type and domain. These approaches are still far from mainstream adoption, possibly because of a lack of good predictors. Inspired by previous research, we advocate that label prediction might benefit from leveraging metrics derived from communication data and social network analysis (SNA) for issues in which social interaction occurs. Thus, we study how these "social metrics"can improve the automatic labeling of open issues with API domains - categories of APIs used in the source code that solves the issue - which the literature shows that newcomers to the project consider relevant for task selection. We mined data from OSS projects' repositories and organized it in periods to reflect the seasonality of the contributors' project participation. We replicated metrics from previous work and added social metrics to the corpus to predict API-domain labels. Social metrics improved the performance of the classifiers compared to using only the issue description text in terms of precision, recall, and F-measure. Precision (0.922) increased by 15.82% and F-measure (0.942) by 15.89% for a project with high social activity. These results indicate that social metrics can help capture the patterns of social interactions in a software project and improve the labeling of issues in an issue tracker.