Characterizing YouTube Channels via Multi-View Content Similarity

YouTube channels communicate their themes and attract viewers through titles, descriptions, transcripts, and categories. While prior research has focused on user engagement, the internal consistency of content across a channel’s videos remains underexplored. This paper presents a framework for characterizing YouTube channels based on semantic similarity among key content features. Using a dataset of 150 channels and 157,235 videos, pairwise similarity scores were computed across six content feature combinations. five unsupervised clustering algorithms were applied to each pair, and results were integrated through majority voting to produce stable channel clusters. Similarity-based analysis revealed recurring alignment patterns, leading to five high-level content behavior characterizations. These profiles reflect distinct strategies in metadata coherence, narrative structure, and category usage. The proposed method enables scalable, label-free, and language-agnostic analysis of channel behavior. By revealing gaps between content and metadata, the framework surfaces potential editorial biases that may disproportionately affect marginalized audiences on social media.