Measuring the progress towards the Sustainable Development Goals (SDGs) requires the collection of relevant and reliable data. To do so, Citizen Science can provide an essential source of non-traditional data for tracking progress towards the SDGs, as well as generate social innovations that enable such progress. At its core, citizen science relies on participatory processes involving the collaboration of stakeholders with diverse standpoints, skills, and backgrounds. The ability to measure these participatory processes is therefore key for the monitoring and evaluation of citizen science projects and to support the decisions of their coordinators. Here, we show that the monitoring of social interaction networks provides unique insights on the participatory processes and outcomes of citizen science projects. We studied fourteen early-stage citizen science projects that participated in an innovation cycle focused on SDG 13, Climate Action, as part of the Crowd4SDG project. We implemented a monitoring strategy to measure the collaborative profiles of citizen science teams. This allowed us to generate dynamic interaction networks across complementary dimensions, making visible both formal and informal interactions associated with the division of labor, collaborations, advice seeking, and communication processes of the projects during their development. Leveraging jury evaluation data, we showed that while team composition and communication are associated with project quality, measures of collaboration and activity are associated with engagement quality. Overall, monitoring social interaction dynamics helps build a more comprehensive picture of participatory processes, which is of importance for guiding citizen science projects and for designing initiatives leveraging citizen science to address the SDGs.