A data-driven analysis of YouTube community features

Abstract

The success of YouTube has profoundly changed the face of industries dealing with digital content as it provides new means of distribution and promotion. While YouTube poses new opportunities for content creators to quickly reach a large audience of viewers, all videos posted online do not compete on the same footing with regard to popularity. To better understand the variation in the popularity of videos, we investigate the role of social interactions between users. In this way, our work is in stark contrast to prior research that studied user generated content video systems but without considering the structure of social relationships within those systems. In this paper, we conduct measurements on YouTube by applying a novel methodology to identify all the users interacting within the same community of interest. Using user information and the meta-data of posted videos, we analyze the influence of the community-based features of YouTube on the popularity of content posted online. Our analysis shows that users posting videos under a specific category get a better recognition than those actively posting videos belonging to a large variety of categories.

Exploring and Analyzing YouTube Communities through Data Mining and Knowledge Graphs

“…YouTube, being one of the largest aggregators of video content, provides a rich source of data that can be analyzed to obtain modeling and analysis of community dynamics. Such studies focus on surveying users (Rotman et al, 2009;Rotman and Preece, 2010) or using metadata (Spathis and Gorcitz, 2011;Che et al, 2015).…”