Abstract-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.

Information resources on the Web like videos, images, and documents are increasingly becoming more "social" through user engagement via commenting systems. These commenting systems provide a forum for users to discuss the resources but have the side effect of providing valuable editorial and contextual information about the resources. In this paper, we explore a comments-driven clustering framework for organizing Web resources according to this user-based perspective. Concretely, we propose a hierarchical comment clustering approach that relies on two key features: (i) comment term normalization and key term extraction for distilling noisy comments for effective clustering; and (ii) a real-time insertion component for incrementally updating the comments-based hierarchy so that resources can be efficiently placed in the hierarchy as comments arise and without the need to re-generate the (potentially) expensive hierarchy. We study the clustering approach over the popular video sharing site YouTube. YouTube is a challenging and difficult environment, notorious for its extremely short, illformed, and often unintelligible user-contributed comments. Through extensive experimental study, we find that the proposed approach can lead to effective and efficient comments-based video organizing even in a YouTube-like environment.

The explosion of the real-time web has spurred a growing need for new methods to organize, monitor, and distill relevant information from these large-scale social streams. One especially encouraging development is the self-curation of the real-time web via user-driven linking, in which users annotate their own status updates with lightweight semantic annotations -or hashtags. Unfortunately, there is evidence that hashtag growth is not keeping pace with the growth of the overall real-time web. In a random sample of 3 million tweets, we find that only 10.2% contain at least one hashtag. Hence, in this paper we explore the possibility of predicting hashtags for un-annotated status updates. Toward this end, we propose and evaluate a graph-based prediction framework. Three of the unique features of the approach are: (i) a path aggregation technique for scoring the closeness of terms and hashtags in the graph; (ii) pivot term selection, for identifying high value terms in status updates; and (iii) a dynamic sliding window for recommending hashtags reflecting the current status of the real-time web. Experimentally we find encouraging results in comparison with Bayesian and data mining-based approaches.

No abstract

No abstract