Research Subject 3:Analyzing and Visualizing Human Society Interaction

Toyoda Group

The emergence of a large and comprehensive data on cities and SNSs allows people to expand their horizon. Our goal is to develop methods for decision making based on information analysis and visualization of the large-scale data, and fundamental technologies for personalized information presentation. We develop information analysis and visualization methods through several analysis tasks for understanding human-society interaction, that is people’s action and behavior on physical and cyber society observed from the large data on cities and SNSs.  As the basis for personalized information presentation, we develop technologies to understand and interpret analysis models (especially natural language processing models based on deep neural network).

For human-society interaction analysis, we have continuously archived large scale mobility data (accumulated GPS data) and SNS data (Twitter data). In response to the spread of COVID-19 that began after the project started, we selected analysis tasks that would contribute to countermeasures. We conducted analysis and visualization tasks related to people’s activities and behavior, such as the correlation between city congestion and infection risk, and changes in their stance toward vaccination. Toward the personalized information presentation, we developed fundamental techniques for interpret and explain natural language processing (NLP) models.  Especially, we analyzed variations in the meanings of words used by individuals, and linguistic phenomena captured by each neuron in a deep neural network model for NLP.