Network Analysis of the iNaturalist Citizen Science Community
This work addresses the understudied dynamics of citizen science projects, offering a new benchmark for network analysis, though it is incremental in applying existing techniques to a specific domain.
The researchers analyzed the structure and interactions within the iNaturalist citizen science community by modeling it as a bipartite network, and they introduced a novel benchmark network from this data that provides unique insights into network science methods through link prediction tasks.
In recent years, citizen science has become a larger and larger part of the scientific community. Its ability to crowd source data and expertise from thousands of citizen scientists makes it invaluable. Despite the field's growing popularity, the interactions and structure of citizen science projects are still poorly understood and under analyzed. We use the iNaturalist citizen science platform as a case study to analyze the structure of citizen science projects. We frame the data from iNaturalist as a bipartite network and use visualizations as well as established network science techniques to gain insights into the structure and interactions between users in citizen science projects. Finally, we propose a novel unique benchmark for network science research by using the iNaturalist data to create a network which has an unusual structure relative to other common benchmark networks. We demonstrate using a link prediction task that this network can be used to gain novel insights into a variety of network science methods.