Exploring social dynamics in online media sharing
Proceedings of the 16th international conference on World Wide Web
I tube, you tube, everybody tubes: analyzing the world's largest user generated content video system
Proceedings of the 7th ACM SIGCOMM conference on Internet measurement
Measurement and analysis of online social networks
Proceedings of the 7th ACM SIGCOMM conference on Internet measurement
Understanding video interactions in youtube
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Vlogcast yourself: nonverbal behavior and attention in social media
International Conference on Multimodal Interfaces and the Workshop on Machine Learning for Multimodal Interaction
Measuring and enhancing the social connectivity of UGC video systems: a case study of YouKu
Proceedings of the Nineteenth International Workshop on Quality of Service
VlogSense: Conversational behavior and social attention in YouTube
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP) - Special section on ACM multimedia 2010 best paper candidates, and issue on social media
Hi-index | 0.00 |
While existing studies on YouTube's massive user-generated video content have mostly focused on the analysis of videos, their characteristics, and network properties, little attention has been paid to the analysis of users' long-term behavior as it relates to the roles they self-define and (explicitly or not) play in the site. In this paper, we present a novel statistical analysis of aggregated user behavior in YouTube from the novel perspective of user categories, a feature that allows people to ascribe to popular roles and to potentially reach certain communities. Using a sample of 270,000 users, we found that a high level of interaction and participation is concentrated on a relatively small, yet significant, group of users, following recognizable patterns of personal and social involvement. Based on our analysis, we also show that by using simple behavioral features from user profiles, people can be automatically classified according to their category with accuracy rates of up to 73%.