| Budget Amount *help |
¥4,160,000 (Direct Cost: ¥3,200,000、Indirect Cost: ¥960,000)
Fiscal Year 2013: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2012: ¥1,820,000 (Direct Cost: ¥1,400,000、Indirect Cost: ¥420,000)
Fiscal Year 2011: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
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| Research Abstract |
In this work, we extracted multiple communities that overlap with each other, but have interest in different things from a large social network on Twitter, a notable microblogging service. To this end, we first extracted information diffusion networks by means of tags and characteristic keywords in articles, as well as results of a topic estimation method (LDA: Latent Dirichlet Allocation). Then, those networks are integrated by means of a graph mining technique that can find frequent patterns from multiple graphs. Namely, information diffusion networks are integrated if they share common substructures whose frequency is equal to or greater than a given threshold. Furthermore, we devised a method of accurately detecting change points in information diffusion sequences around which diffusion speed has changed in order to investigate the features of resulting communities.
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