The Transnational Happiness Study with Big Data Technology

Editorial NotesThe editors have requested minor, non-substantive changes to the VoR and, in accordance with ACM policies, a Corrected VoR was published on February 9, 2021. For reference purposes the VoR may still be accessed via the Supplemental Material section on this page.AbstractHappiness is a hot topic in academic circles. The study of happiness involves many disciplines, such as philosophy, psychology, sociology, and economics. However, there are few studies on the quantitative analysis of the factors affecting happiness. In this article, we used the well-known World Values Survey Wave 6 (WV6) dataset to quantitatively analyze the happiness of 57 countries with Big Data techniques. First, we obtained the seven most important factors by constructing happiness decision trees for each country. Calculating the frequencies of these factors, we obtained the 17 most important indicators for the prediction of happiness in the world. Then, we selected five representative countries, namely, Sweden, Japan, India, China, and the USA, and analyzed the indicators with the random forest method. We identified different patterns of factors that influence happiness in different countries. This study is a successful attempt to apply data mining technology in the social sciences, and the results are of practical significance.