Abstract
Social media presents unique challenges for topic classification, including the brevity of posts, the informal nature of conversations, and the frequent reliance on external hyperlinks to give context to a conversation. In this paper we investigate the usefulness of these external hyperlinks for determining the topic of an individual post. We focus specifically on hyperlinks to objects which have related metadata available on the Web, including Amazon products and YouTube videos. Our experiments show that the inclusion of metadata from hyperlinked objects in addition to the original post content improved classifier performance measured with the F-score from 84% to 90%. Further, even classification based on object metadata alone outperforms classification based on the original post content.
The work presented in this paper has been funded in part by Science Foundation Ireland under Grant No. SFI/08/CE/I1380 (Lion-2).
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Kinsella, S., Passant, A., Breslin, J.G. (2011). Topic Classification in Social Media Using Metadata from Hyperlinked Objects. In: Clough, P., et al. Advances in Information Retrieval. ECIR 2011. Lecture Notes in Computer Science, vol 6611. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20161-5_20
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DOI: https://doi.org/10.1007/978-3-642-20161-5_20
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-20160-8
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