Tadanobu Furukawa

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This paper presents a study of the various aspects of blog reading behavior. The analyzed data are obtained from a Japanese weblog hosting service, Doblog. Four kinds of social networks are generated and analyzed: citation , comment, trackback, and blogroll networks. In addition, the user log data are used to identify readership relations among bloggers.(More)
This study investigates Weblog users' behavioral data on a hosting service in Japan. The study analyzes users' behaviors, such as browsing and bookmarking, in addition to posting comments and sending trackbacks. Results of this study reveal the causes of a user's visiting and regularly browsing behaviors, especially from a social network point of view. The(More)
The diffusion process on weblogs has attracted great interest since the early days of weblog studies. We propose a ranking technique which extracts topics and innovators by analyzing that process. Our method identifies URLs of topics and the bloggers who trigger topic diffusion. Our assumption is that the strength of propagation of a topic is determined by(More)
How can we automatically determine which skills must be mastered for the successful completion of an online course? Large-scale online courses (e.g., MOOCs) often contain a broad range of contents frequently intended to be a semester's worth of materials; this breadth often makes it difficult to articulate an accurate set of skills and knowledge (i.e., a(More)
We propose an algorithm to predict users' future bookmarking using social bookmarking data. It is a problem that primitive collaborative filtering cannot exactly catch users' preferences in social bookmarkings containing enormous items (URLs) because in many cases user's adoption data is sparse. There can be various influences on bookmarking such as effects(More)
In a blog network, there are many relations such as comment, trackback, and so on. We consider that if the relations are related to user's reading activity, we can extract useful information from the relations for using a recommendation system. We define the strength and type as the measure for relations, and analyze the correlation between those measures(More)
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