Details
| Original language | English |
|---|---|
| Article number | 17 |
| Journal | ACM transactions on the web |
| Volume | 8 |
| Issue number | 3 |
| Publication status | Published - 8 Jul 2014 |
Abstract
An analysis of the social video sharing platform YouTube and the news aggregator Yahoo! News reveals the presence of vast amounts of community feedback through comments for published videos and news stories, as well as through metaratings for these comments. This article presents an in-depth study of commenting and comment rating behavior on a sample of more than 10 million user comments on YouTube and Yahoo! News. In this study, comment ratings are considered first-class citizens. Their dependencies with textual content, thread structure of comments, and associated content (e.g., videos and their metadata) are analyzed to obtain a comprehensive understanding of the community commenting behavior. Furthermore, this article explores the applicability of machine learning and data mining to detect acceptance of comments by the community, comments likely to trigger discussions, controversial and polarizing content, and users exhibiting offensive commenting behavior. Results from this study have potential application in guiding the design of community-oriented online discussion platforms. 2014 Copyright held by the Owner/Author.
Keywords
- Comment ratings, Community feedback, Yahoo! News, YouTube
ASJC Scopus subject areas
- Computer Science(all)
- Computer Networks and Communications
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In: ACM transactions on the web, Vol. 8, No. 3, 17, 08.07.2014.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Analyzing and Mining Comments and Comment Ratingson the Social Web
AU - Siersdorfer, Stefan
AU - Chelaru, Sergiu
AU - San Pedro, Jose
AU - Altingovde, Ismail Sengor
AU - Nejdl, Wolfgang
PY - 2014/7/8
Y1 - 2014/7/8
N2 - An analysis of the social video sharing platform YouTube and the news aggregator Yahoo! News reveals the presence of vast amounts of community feedback through comments for published videos and news stories, as well as through metaratings for these comments. This article presents an in-depth study of commenting and comment rating behavior on a sample of more than 10 million user comments on YouTube and Yahoo! News. In this study, comment ratings are considered first-class citizens. Their dependencies with textual content, thread structure of comments, and associated content (e.g., videos and their metadata) are analyzed to obtain a comprehensive understanding of the community commenting behavior. Furthermore, this article explores the applicability of machine learning and data mining to detect acceptance of comments by the community, comments likely to trigger discussions, controversial and polarizing content, and users exhibiting offensive commenting behavior. Results from this study have potential application in guiding the design of community-oriented online discussion platforms. 2014 Copyright held by the Owner/Author.
AB - An analysis of the social video sharing platform YouTube and the news aggregator Yahoo! News reveals the presence of vast amounts of community feedback through comments for published videos and news stories, as well as through metaratings for these comments. This article presents an in-depth study of commenting and comment rating behavior on a sample of more than 10 million user comments on YouTube and Yahoo! News. In this study, comment ratings are considered first-class citizens. Their dependencies with textual content, thread structure of comments, and associated content (e.g., videos and their metadata) are analyzed to obtain a comprehensive understanding of the community commenting behavior. Furthermore, this article explores the applicability of machine learning and data mining to detect acceptance of comments by the community, comments likely to trigger discussions, controversial and polarizing content, and users exhibiting offensive commenting behavior. Results from this study have potential application in guiding the design of community-oriented online discussion platforms. 2014 Copyright held by the Owner/Author.
KW - Comment ratings
KW - Community feedback
KW - Yahoo! News
KW - YouTube
UR - http://www.scopus.com/inward/record.url?scp=84904122751&partnerID=8YFLogxK
U2 - 10.1145/2628441
DO - 10.1145/2628441
M3 - Article
AN - SCOPUS:84904122751
VL - 8
JO - ACM transactions on the web
JF - ACM transactions on the web
SN - 1559-1131
IS - 3
M1 - 17
ER -