My joint work with Stefan Siersdorfer “How useful are your comments? – Analyzing and Predicting YouTube Comments and Comment Ratings” has been accepted as a full paper in the next edition of ACM Conference on the World Wide Web (WWW’10).
Find below the abstract of the submission:
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.
My submission to SIGIR 2009 has been accepted as a full paper. The article, entitled “Automatic Video Tagging using Content Redundancy”, proposes an interesting approach to the exploitation of redundant content in folksonomies. We consider the specific case of the leading video sharing website, YouTube. CBCR techniques are used to automatically detect duplication in the video collection, and several metadata propagation methods are proposed to spread community knowledge around the graph of resources.
The abstract of the paper follows below:
“The analysis of the leading social video sharing platform YouTube reveals a high amount of redundancy, in the form of videos with overlapping or duplicated content. In this paper, we show that this redundancy can provide useful information about connections between videos. We reveal these links using robust content-based video analysis techniques and exploit them for generating new tag assignments. To this end, we propose different tag propagation methods for automatically obtaining richer video annotations. Our techniques provide the user with additional information about videos, and lead to enhanced feature representations for applications such as automatic data organization and search. Experiments on video clustering and classification as well as a user evaluation demonstrate the viability of our approach.”
My joint work with Stefan Siersdorfer “Ranking and Classifying Attractiveness of Photos in Folksonomies” has been accepted as a full paper in the next edition of ACM Conference on the World Wide Web (WWW’09).
In this paper, we propose a novel methodology to derive a metric of photo attractiveness in a completely automatic manner taking advantage of user generated data in Flickr (namely metadata and user feedback statistics) as well as image visual features. The paper can be downloaded from the conference site using the following link. Slides for the presentation will be soon available from the same site.
Find below the abstract of the submission:
“Web 2.0 applications like Flickr, YouTube, or Del.icio.us are increasingly popular online communities for creating, editing and sharing content. The growing size of these folksonomies poses new challenges in terms of search and data mining. In this paper we introduce a novel methodology for automatically ranking and classifying photos according to their attractiveness for folksonomy members. To this end, we exploit image features known for having signiﬁcant effects on the visual quality perceived by humans (e.g. sharpness and colorfulness) as well as textual meta data, in what is a multi-modal approach. Using feedback and annotations available in the Web 2.0 photo sharing system Flickr, we assign relevance values to the photos and train classiﬁcation and regression models based on these relevance assignments. With the resulting machine learning models we categorize and rank photos according to their attractiveness. Applications include enhanced ranking functions for search and recommender methods for attractive content. Large scale experiments on a collection of Flickr photos demonstrate the viability of our approach.”