Details
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 19th International Conference on World Wide Web, WWW '10 |
| Pages | 891-900 |
| Number of pages | 10 |
| Publication status | Published - 26 Apr 2010 |
| Event | 19th International World Wide Web Conference, WWW2010 - Raleigh, NC, United States Duration: 26 Apr 2010 → 30 Apr 2010 |
Publication series
| Name | Proceedings of the 19th International Conference on World Wide Web, WWW '10 |
|---|
Abstract
An analysis of the social video sharing platform YouTube reveals a high amount of community feedback through comments for published videos as well as through meta ratings for these comments. In this paper, we present an in-depth study of commenting and comment rating behavior on a sample of more than 6 million comments on 67,000 YouTube videos for which we analyzed dependencies between comments, views, comment ratings and topic categories. In addition, we studied the influence of sentiment expressed in comments on the ratings for these comments using the SentiWordNet thesaurus, a lexical WordNet-based resource containing sentiment annotations. Finally, to predict community acceptance for comments not yet rated, we built different classifiers for the estimation of ratings for these comments. The results of our large-scale evaluations are promising and indicate that community feedback on already rated comments can help to filter new unrated comments or suggest particularly useful but still unrated comments.
Keywords
- comment ratings, community feedback, YouTube
ASJC Scopus subject areas
- Computer Science(all)
- Computer Networks and Communications
- Computer Science(all)
- Computer Science Applications
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Proceedings of the 19th International Conference on World Wide Web, WWW '10. 2010. p. 891-900 (Proceedings of the 19th International Conference on World Wide Web, WWW '10).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - How Useful are Your Comments? Analyzing and Predicting YouTube Comments and Comment Ratings
AU - Siersdorfer, Stefan
AU - Chelaru, Sergiu
AU - Nejdl, Wolfgang
AU - San Pedro, Jose
PY - 2010/4/26
Y1 - 2010/4/26
N2 - An analysis of the social video sharing platform YouTube reveals a high amount of community feedback through comments for published videos as well as through meta ratings for these comments. In this paper, we present an in-depth study of commenting and comment rating behavior on a sample of more than 6 million comments on 67,000 YouTube videos for which we analyzed dependencies between comments, views, comment ratings and topic categories. In addition, we studied the influence of sentiment expressed in comments on the ratings for these comments using the SentiWordNet thesaurus, a lexical WordNet-based resource containing sentiment annotations. Finally, to predict community acceptance for comments not yet rated, we built different classifiers for the estimation of ratings for these comments. The results of our large-scale evaluations are promising and indicate that community feedback on already rated comments can help to filter new unrated comments or suggest particularly useful but still unrated comments.
AB - An analysis of the social video sharing platform YouTube reveals a high amount of community feedback through comments for published videos as well as through meta ratings for these comments. In this paper, we present an in-depth study of commenting and comment rating behavior on a sample of more than 6 million comments on 67,000 YouTube videos for which we analyzed dependencies between comments, views, comment ratings and topic categories. In addition, we studied the influence of sentiment expressed in comments on the ratings for these comments using the SentiWordNet thesaurus, a lexical WordNet-based resource containing sentiment annotations. Finally, to predict community acceptance for comments not yet rated, we built different classifiers for the estimation of ratings for these comments. The results of our large-scale evaluations are promising and indicate that community feedback on already rated comments can help to filter new unrated comments or suggest particularly useful but still unrated comments.
KW - comment ratings
KW - community feedback
KW - YouTube
UR - http://www.scopus.com/inward/record.url?scp=77954567494&partnerID=8YFLogxK
U2 - 10.1145/1772690.1772781
DO - 10.1145/1772690.1772781
M3 - Conference contribution
AN - SCOPUS:77954567494
SN - 9781605587998
T3 - Proceedings of the 19th International Conference on World Wide Web, WWW '10
SP - 891
EP - 900
BT - Proceedings of the 19th International Conference on World Wide Web, WWW '10
T2 - 19th International World Wide Web Conference, WWW2010
Y2 - 26 April 2010 through 30 April 2010
ER -