Social Computing
Research Staff
-
Professor
Eiji ARAMAKI -
Associate Professor
Shoko WAKAMIYA -
Affiliate Associate Professor
Kaoru SAGARA -
Affiliate Associate Professor
Shuntaro YADA -
Assistant Professor
Shaowen PENG -
Affiliate Assistant Professor
SHE Wan Jou
Research Areas
Fig.1：The Social Computing Laboratory.
Fig.2: Radiography report search system.
Fig.3: Twitter-based influenza surveillance system “Hay fever radar” (top) and word clouds of multilingual tweets related to COVID-19.
Social Computing
The Social Computing Laboratory was established in September 2015 to pursue cutting-edge research activities. Positioned under the umbrella of the Division of Information Science since April 2020, our laboratory is engaged in interdisciplinary research and education in a new scientific arena.
Our core technology is natural language processing (NLP), but we aggressively employ and collaborate with other fields to produce extensive applications, mainly in the medical and healthcare fields. Join us, and let’s break new ground together (Fig. 1).
NLP, web, medical & more
The mission of the Social Computing Laboratory is to explore a new interdisciplinary branch of informatics that is both practical and theoretical. Our research interests relate to healthcare and other real-life challenges, as well as to the application of NLP and other information retrieval techniques.
Our approaches are interdisciplinary and practical. We address practical problems in collaboration with experts from various fields, including informatics, medicine, biology, linguistics, psychology, and sociology. In addition to practical informatics applications, scientific rigor is a major interest.
Research: Natural Language Processing + medical practice
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 bring more precise and timelier treatments in this field. One of our goals is to promote and support the implementation of practical tools and systems into the medical industry (Fig. 2). We have also been developing and publishing various corpus to accelerate medical NLP research and the development of medical AI.
Research: Web mining
Big data on the web, including social media (e.g., Twitter) data are regarded as valuable information resources for various applications. One of our representative applications is disease surveillance systems for influenza, hay fever, COVID-19, etc., by extracting and analyzing social media data and search query log data related to the diseases and symptoms (Fig. 3). We are also conducting various studies using social media data, such as bot detection, fake news or rumor detection, and offensive language detection, etc. We have been applying various technologies from our familiar NLP field and other fields, such as simulation modeling and psychological modeling.