Social networks play important roles in the Semantic Web: knowledge management, information retrieval, ubiquitous computing, and so on. We propose a social network extraction system called POLY-PHONET, which employs several advanced techniques to extract relations of persons, detect groups of persons, and obtain keywords for a person. Search engines, especially Google, are used to measure co-occurrence of information and obtain Web documents.Several studies have used search engines to extract social networks from the Web, but our research advances the following points: First, we reduce the related methods into simple pseudocodes using Google so that we can build up integrated systems. Second, we develop several new algorithms for social networking mining such as those to classify relations into categories, to make extraction scalable, and to obtain and utilize person-to-word relations. Third, every module is implemented in POLYPHONET, which has been used at four academic conferences, each with more than 500 participants. We overview that system. Finally, a novel architecture called Super Social Network Mining is proposed; it utilizes simple modules using Google and is characterized by scalability and Relate-Identify processes: Identification of each entity and extraction of relations are repeated to obtain a more precise social network.
The World Wide Web supports new styles of creative activities. For this study, we investigate massively collaborative creation via the Web, by which numerous people gather to evolve their works collaboratively. Nico Nico Douga is a video sharing website, where many videos are created collaboratively. We specifically examine Hatsune Miku, a version of singing synthesizer application software that has inspired not only song creation but also songwriting, illustration, and video editing. As described herein, different types of creators interact to create new contents though their social network. Using tags, we classified videos and creators on Nico Nico Douga automatically into four basic categories. Thereby, we produced a social network from relationships among videos and creators by analyzing videos' descriptions. The social network reveals interesting features. Different categories of creators serve different roles in evolving the network, e.g., songwriters gather more links than other categories, implying that they are triggers to network evolution. We also extracted communities from the network and observed different community structures. One is a centralized network in which a single songwriter is central and others are peripheral. The other is a messier network, in which some illustrators are central, but the centrality is weak.
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