Towards a taxonomy of waste management research: An application of community detection in keyword network

Abstract

Our global environment is facing significant waste and waste management challenges, despite minor differences in definitions and what constitutes waste. In recent decades, waste has been the subject of a great deal of research since civilized society needs to manage waste efficiently and effectively. As a result of all of these issues, researchers have focused on improving waste management. To do so, a taxonomy based on the research conducted on waste management will help them. Research taxonomies and visualizations help us find the most important areas of research in waste management. There are a few researchers who taxonomize research waste according to their methodology, but there is no quantitative and data-oriented approach. By using community detection and social network analysis, this study aims to quantify and systematize waste area taxonomy.There have been 9095 articles published on waste management since 2000, and these articles were retrieved from the WOS database to create a keyword network made up of 17,808 members (unique keywords) and 79,763 links in this study. To discover the research areas of waste management, the community detection technique was used in the keyword network, and 10 main communities that represent the main research areas of waste management were discovered and named based on waste management characteristics and waste types. These 10 communities include more than 90% of all waste management research.In the next step and to achieve a hierarchical taxonomy of waste management studies, the community detection algorithm was implemented in ten communities and by discovering sub-communities, a waste management taxonomy with 10 main branches and 26 sub-branches was presented.