Social Computing

Research Staff

  • Assoc.Prof. Eiji Aramaki

    Eiji Aramaki

  • Assist.Prof. Shoko Wakamiya

    Shoko Wakamiya

E-mail { aramaki, wakamiya }[at]

Research Areas

Social Computing

The Social Computing Laboratory, NAIST, was established in September 2015 to pursue cutting-edge research activities. Our laboratory is engaged in interdisciplinary research and education in a new scientific arena.
Our core technology is natural language processing, but we aggressively employ and collaborate with other fields in order to produce extensive applications mainly in the medical and healthcare fields. Join us, and let's break new ground together.


The mission of the Social Computing Laboratory is to explore a new interdisciplinary branch of informatics that is both practical and theoretical. Its research interests relate to healthcare and other real-life challenges, as well as to the application of natural language processing (NLP) and other information retrieval techniques.
Our approaches are:
Interdisciplinary and practical: we address practical problems in collaboration with experts from a wide range of fields, including informatics, medicine, biology, linguistics, psychology, and sociology.
Theoretical: in addition to practical informatics applications, scientific rigor is our major interest.

Research: Natural Language Processing + Medical

Electronic medical records are now replacing traditional paper medical records, and accordingly, the importance of information processing techniques in medical fields has been increasing rapidly. ICT enables us to analyze voluminous medical records and obtain knowledge from the analysis, which would definitely bring more precise and timelier treatments in this field. Such assistance has much potential in saving more lives and further improving life quality. One of our goal is to promote and support the implementation of practical tools and systems into the medical industry.

Research: Web mining

Social Network Services (SNS) potentially serve as valuable information resources for various applications. We have addressed and will be addressing web-based applications. For example, to date, most web-based disease surveillance systems assume that the web immediately reflects real disease conditions. However, such systems, in fact, suffer from time lags between people’s web actions and real-time situations. We have taken this time gap into consideration and have been applying various technologies not only from our familiar NLP field, but also from other fields, such as simulation modeling and psychological modeling. Findings from this study will also directly contribute to healthcare.

Fig.1: Web-based disease surveillance system “KAZE-MIRU”

Fig.1: Web-based disease surveillance system "KAZE-MIRU".

Fig.2: We built the collection of elder's narratives

Fig.2: We built a collection of elder's narratives.

Fig.3: Our fields

Fig.3: Our fields.