A paper submitted to the 6th IEEE/ACM International Conference on Big Data Computing, Applications and Technologies (BDCAT ’19), authored by Sota Kato, Takafumi Nakanishi, Hirokazu Shimauchi, and Budrul Ahsan of the Tokyo Foundation for Policy Research, was named, along with two other submissions, as a “best paper candidate” at the conference—held on December 2–5 in Auckland, New Zealand—and received an invitation for publication in the 2020 edition of the Journal of Cloud Computing, published by Springer.

The paper, titled, “Topic Variation Detection Method for Detecting Political Business Cycles,” introduces a novel method of analyzing 32 years’ of text data detailing the Japanese prime minister’s daily activities to enable an empirical analysis of the “political business cycle.”