Communications of the ACM
Flaming in electronic communication
Decision Support Systems
Measurement and analysis of online social networks
Proceedings of the 7th ACM SIGCOMM conference on Internet measurement
Structure and Network in the YouTube Core
HICSS '08 Proceedings of the Proceedings of the 41st Annual Hawaii International Conference on System Sciences
Why we twitter: understanding microblogging usage and communities
Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 workshop on Web mining and social network analysis
Information-centered research for large-scale analyses of new information sources
Journal of the American Society for Information Science and Technology
Social Science Computer Review
Emoticons and Online Message Interpretation
Social Science Computer Review
Identifying user behavior in online social networks
Proceedings of the 1st Workshop on Social Network Systems
Republic.com 2.0
Crowdsourcing, attention and productivity
Journal of Information Science
Analyzing the video popularity characteristics of large-scale user generated content systems
IEEE/ACM Transactions on Networking (TON)
Perspectives on social tagging
Journal of the American Society for Information Science and Technology
Social Science Computer Review
Public dialogs in social network sites: What is their purpose?
Journal of the American Society for Information Science and Technology
What is Twitter, a social network or a news media?
Proceedings of the 19th international conference on World wide web
How useful are your comments?: analyzing and predicting youtube comments and comment ratings
Proceedings of the 19th international conference on World wide web
Sentiment in short strength detection informal text
Journal of the American Society for Information Science and Technology
Journal of the American Society for Information Science and Technology
Can social features help learning to rank youtube videos?
WISE'12 Proceedings of the 13th international conference on Web Information Systems Engineering
Damping sentiment analysis in online communication: discussions, monologs and dialogs
CICLing'13 Proceedings of the 14th international conference on Computational Linguistics and Intelligent Text Processing - Volume 2
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YouTube is one of the world's most popular websites and hosts numerous amateur and professional videos. Comments on these videos might be researched to give insights into audience reactions to important issues or particular videos. Yet, little is known about YouTube discussions in general: how frequent they are, who typically participates, and the role of sentiment. This article fills this gap through an analysis of large samples of text comments on YouTube videos. The results identify patterns and give some benchmarks against which future YouTube research into individual videos can be compared. For instance, the typical YouTube comment was mildly positive, was posted by a 29-year-old male, and contained 58 characters. About 23% of comments in the complete comment sets were replies to previous comments. There was no typical density of discussion on YouTube videos in the sense of the proportion of replies to other comments: videos with both few and many replies were common. The YouTube audience engaged with each other disproportionately when making negative comments, however; positive comments elicited few replies. The biggest trigger of discussion seemed to be religion, whereas the videos attracting the least discussion were predominantly from the Music, Comedy, and How to & Style categories. This suggests different audience uses for YouTube, from passive entertainment to active debating. © 2012 Wiley Periodicals, Inc.