Analyzing and Mining Comments and Comment Ratings on the Social Web

Date

2014-06-01

Author

SİERSDORFER, Stefan
CHELARU, Sergiu
Pedro, Jose San
Altıngövde, İsmail Sengör
NEJDL, Wolfgang

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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.

Subject Keywords

Computer Networks and Communications

URI

https://hdl.handle.net/11511/42360

Journal

ACM TRANSACTIONS ON THE WEB

DOI

https://doi.org/10.1145/2628441

Collections

Department of Computer Engineering, Article

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Citation Formats

S. SİERSDORFER, S. CHELARU, J. S. Pedro, İ. S. Altıngövde, and W. NEJDL, “Analyzing and Mining Comments and Comment Ratings on the Social Web,” ACM TRANSACTIONS ON THE WEB, pp. 0–0, 2014, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/42360.