Computational Social Science, CSS

The objective of the Computational Social Science track is to collect and analyze digital data, keeping pace with rapid developments in society and technology, to pioneer a new field in the social sciences. With social activity increasingly moving to the interconnected cyberspace in modern society, the amount of digital data generated as a result of such activity (big data) is also exponentially growing. At the same time, computing power to process such big data is also growing, reducing the computing cost involved in machine learning, simulations and advanced statistics and generating more convenient ways to apply them to research. The Computational Social Science track aims to utilize technologies in digital data generation and processing to provide new insight into society.

Research topics
  • Knowledge structure research using quantitative methodology
  • Analysis and problem-solving for communication in modern society using transdisciplinary research
  • Sustainable energy-environment-society nexus
  • Analysis of the structures of sociospatial inequalty in modern cities
  • Digital analysis of qualitative data
Participating Professors