We study how an online community perceives the relative quality of its own user-contributed content, which has important implications for the successful self-regulation and growth of the Social Web in the presence of increasing spam and a flood of Social Web metadata. We propose and evaluate a machine learning-based approach for ranking comments on the Social Web based on the community's expressed preferences, which can be used to promote high-quality comments and filter out low-quality comments. We study several factors impacting community preference, including the contributor's reputation and community activity level, as well as the complexity and richness of the comment. Through experiments, we find that the proposed approach results in significant improvement in ranking quality versus alternative approaches.
Comment Ranking: Hsu et al. REF developed a regression model for identifying and ranking comments within a Social Web community based on the community's expressed preferences.
Ranking Comments on the Social Web
2009 International Conference on Computational Science and Engineering
2009 International Conference on Computational Science and Engineering
Computer Science