Tube2Vec: Social and Semantic Embeddings of YouTube Channels

TL;DR

This paper introduces machine learning-based embeddings for YouTube channels that capture social sharing behavior and semantic content, providing a scalable alternative to manual annotation for analyzing YouTube data.

Contribution

It presents a novel method for creating social and semantic embeddings of YouTube channels using large-scale data and machine learning, enabling richer analysis.

Findings

Recommendation embeddings effectively capture social and semantic dimensions.

Social-sharing embeddings correlate with partisan scores.

Embeddings for 44,000 channels are publicly available.

Abstract

Research using YouTube data often explores social and semantic dimensions of channels and videos. Typically, analyses rely on laborious manual annotation of content and content creators, often found by low-recall methods such as keyword search. Here, we explore an alternative approach, using latent representations (embeddings) obtained via machine learning. Using a large dataset of YouTube links shared on Reddit; we create embeddings that capture social sharing behavior, video metadata (title, description, etc.), and YouTube's video recommendations. We evaluate these embeddings using crowdsourcing and existing datasets, finding that recommendation embeddings excel at capturing both social and semantic dimensions, although social-sharing embeddings better correlate with existing partisan scores. We share embeddings capturing the social and semantic dimensions of 44,000 YouTube channels for the benefit of future research on YouTube: https://github.com/epfl-dlab/youtube-embeddings.