Abstract
Online social media systems (such as YouTube or Reddit) provide commenting features to support augmentation of social objects (e.g. video clips or news articles). Unfortunately, many comments are not useful due to the varying intentions of the authors of comments as well as the perspectives of the readers. In this paper, we present, a framework and Web-based system for adaptive faceted ranking of social media comments, which enables users to explore different facets (e.g., subjectivity or topics) and select combinations of facets in order to extract and rank comments that match their interests and are useful for them. Based on an evaluation of the framework, we find that adaptive faceted ranking shows significant improvements over prevalent ranking methods, utilized by many platforms, with respect to the users’ preferences. Demo: http://amowa.cs.univie.ac.at:8080/Frontend/
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References
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© 2015 Springer International Publishing Switzerland
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Momeni, E., Braendle, S., Adar, E. (2015). Adaptive Faceted Ranking for Social Media Comments. In: Hanbury, A., Kazai, G., Rauber, A., Fuhr, N. (eds) Advances in Information Retrieval. ECIR 2015. Lecture Notes in Computer Science, vol 9022. Springer, Cham. https://doi.org/10.1007/978-3-319-16354-3_86
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DOI: https://doi.org/10.1007/978-3-319-16354-3_86
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-16353-6
Online ISBN: 978-3-319-16354-3
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