As a major platform for global content dissemination, YouTube hosts thousands of channels covering diverse topics and attracting varied user engagement. Understanding the behavioral characteristics of these channels is essential for comparative analysis and broader platform insights. While channel-level activity can be studied through multiple signals, the comment section offers a particularly rich source of user interaction data. Patterns in commenter behavior provide an effective basis for characterizing channels and identifying similarities across them. This study introduces a framework for characterizing YouTube channels based on commenter activity, using a dataset of 70 channels that includes 711,301 videos, 13,221,243 commenters, and 129,653,609 comments and covers topics such as U.S. military affairs, geopolitical developments, news, and randomly selected content. The methodology combines co-commenter network construction, clique extraction, feature aggregation, pairwise feature analysis, clustering methods, majority voting, and Euclidean distance measurement to uncover patterns in channel behavior. We define five distinct characterizations based on key commenter activity features such as posting patterns, content diversity, and engagement structure. By relying on unsupervised methods and a combination of structural and behavioral features, this approach enables large-scale, label-free comparison of channels. It provides a flexible framework for identifying groups of channels with similar audience dynamics, offering insights that can support better channel characterization and future studies in social media behavior.