Naohiro Matsumura

The dynamism of 2channel

Abstract“2channel” is the most popular online-community site in Japan, where millions of people are discussing or chitchatting about various topics. The communication in 2channel shows dynamic social phenomena such as positive/negative communication, polarization of opinions, slander called flaming, etc. In this paper, we assume the existence of underlying prevailing structures that motivate people’s participation. The structural equation model of 2channel, which is obtained on the basis of observed collective actions about people’s thought, emotions and motivations, shows the uniformity and regularities in complex human communication in 2channel.

Topic Diffusion in a Community

People are easily affected by others’ comments, especially if they include topics interesting to us. In other words, interesting topics diffuse from person to person in a community. In this chapter, I consider ‘influence’ as a unit of diffusion, and propose the influence diffusion model (IDM) to find valuable information such as influential comments, opinion leaders, and interesting terms from the archives of text-based communication. The IDM is applied to the archives stored in the Yahoo!JAPAN Message Boards, and the results of the experimental evaluation are presented.

PAI: automatic indexing for extracting asserted keywords from a document

This paper proposes an automatic indexing method named PAI (Priming Activation Indexing) that extracts keywords expressing the author’s main point from a document based on the priming effect. The basic idea is that since the author writes a document emphasizing his/her main point, impressive terms born in the mind of the reader could represent the asserted keywords. Our approach employs a spreading activation model without using corpus, thesaurus, syntactic analysis, dependency relations between terms or any other knowledge except for stop-word list. Experimental evaluations are reported by applying PAI to journal/conference papers.

Discovery of Emerging Topics between Communities on WWW

In the real world, discovering new topics covering profitable items and ideas (e.g., mobile phone, global warming, human genome project, etc) is important and interesting. However, since we cannot completely encode the world surrounding us, its difficult to detect such topics and their mechanisms in advance. In order to support the detection, we show a method for revealing the structure of WWW by using the KeyGraph algorithm. Empirical results are reported.

Future Directions of Communities on the Web

Discovering new topics which cover new items, problems, and ideas (e.g., mobile phone, global warming, human genome project, etc) is truly profitable, important, and interesting for us. For instance, 1. Companies producing mobile phones have made large profits by the great sales, 2. The awareness of global warming has improved the environment of the earth by regulating exhaust emissions, 3. Fatal illnesses might be conquered by the human genome project. However, since we cannot completely decode the world surrounding us, we cannot know the topics and their mechanisms in advance. Considering this situation, these phenomena could be a big chance for our activities. In this paper, we describe our approach for discovering the future directions of communities on the web to detect chances.

Combination retrieval for creating knowledge from sparse document-collection

With the variety of human life, people are interested in various matters for each ones unique reason, for which a machine maybe a better counselor than a human. This paper proposes to help user create novel knowledge by combining multiple existing documents, even if the document-collection is sparse, i.e. if a query in the domain has no corresponding answer in the collection. This novel knowledge realizes an answer to a users unique question, which cannot be answered by a single recorded document. In the Combination Retriever implemented here, cost-based abduction is employed for selecting and combining appropriate documents for making a readable and context-reflecting answer. Empirically, Combination Retriever obtained satisfactory answers to users unique questions.

Discovering promising new topics on the WWW

The phenomenon that a little topic can make a big difference is called The Tipping Point. As a strategy for marketing this phenomenon could be a great economic opportunity. For instance, at is a good strategy to promote items associated with a certain fashion. The Tipping Point refers to a situation where the topic matches potential needs of customers. We analyze the mechanism of The Tipping Point in the context of the WWW, and present an algorithm called Expected Activation for discovering promising new topics on the WWW.

Combination Retrieval for Creating Knowledge from Sparse Document Collection

With the variety of human life, people are interested in various matters for each ones unique reason, for which a machine maybe a better counselor than a human. This paper proposes to help user create novel knowledge by combining multiple existing documents, even if the document collection is sparse i.e. if a query in the domain has no corresponding answer in the collection. This novel knowledge realizes an answer to a users unique question, which can not be answered by a single recorded document. In the Combination Retriever implemented here, cost-based abduction is employed for selecting and combining appropriate documents for making a readable and context-reflecting answer. Empirically, Combination Retriever obtained satisfactory answers to users unique questions.

Discovering Seeds of New Interest Spread from Premature Pages Cited by Multiple Communities

The World Wide Web is a great source of new topics significant for trend birth and creation. In this paper, we propose a method for discovering topics, which stimulate communities of people into earnest communications on the topics meaning, and grow into a trend of popular interest. Here, the obtained are web pages which absorb attentions of people from multiple interest-communities. It is shown by a experiments to a small group of people, that topics in such pages can trigger the growth of peoples interests, beyond the bounds of existing communities.

12 Mining scenarios for hepatitis B and C

Publisher Summary As a tool for chance discovery, scenario emergence is useful for real-world decisions. In the real world, events are dynamic and unpredictable, and decision makers are required to make decisions promptly when they believe that a chance—a significant event—may be taking place. This chapter presents an application of scenario emergence involving the discovery and representation of triggering events of essential scenarios in the domain of hepatitis progress and treatment. In the method discussed in the chapter, the widening of a users views was aided by the projection of the users personal experiences on an objective scenario map that displayed a selection of contexts in the wider real world. The process of narrowing the data involved focusing the users concerns and employing the TextDrop tool. The newer the symptoms and the more ambiguous the future, the more useful will be the map. Tools are being developed to make the double helix process of discovery more efficient by combining the functions of KeyGraph and TextDrop and further integrating with visual interfaces to enable the emerging concerns of users to feedback into new cycles of the spiral discovery process.