• Proceedings of the Korean Information Science Society Conference
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• 2010.11a
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• pp.48-49
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• 2010

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.11
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• pp.1121-1125
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• 2010

This paper proposes a Wikipedia-based feedback method for in-depth blog distillation whose goal is to find blogs that represent in-depth thoughts or analysis on a given query. The proposed method uses Wikipedia articles which are relevant to the query. TREC Blogs08 collection which is a large-scale blog corpus and English Wikipedia dump were used for experiments, The proposed method significantly increased the retrieval performance including MAP over the conventional post based feedback method.

• Journal of KIISE:Databases
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• v.37 no.5
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• pp.258-266
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• 2010

In this paper, we introduce a novel method to predict next update of blogs. The number of RSS feeds registered on meta-blogs is on the order of several million. Checking for updates is very time consuming and imposes a heavy burden on network resources. Since blog search engine has limited resources, there is a fix number of blogs that it can visit on a day. Nevertheless we need to maximize chances of getting new data, and the proposed method which predicts update probability on blogs could bring better chances for it. Also this work is important to avoid distributed denial-of-service attack for the owners of blogs. Furthermore, for the internet as whole this work is important, too, because our approach could minimize traffic. In this study, we assumed that there is a specific pattern to when a blogger is actively posting, in terms of days of the week and, more specifically, hours of the day. We analyzed 15,119 blogs to determine a blogger's posting preference. This paper proposes a method to predict the update probability based on a blogger's posting history and preferred days of the week. We applied proposed method to 12,115 blogs to check the precision of our predictions. The evaluation shows that the model has a precision of 0.5 for over 93.06% of the blogs examined.