Towards an Understanding of the Critical Success Factors that Drive Local Community Gatherings in U.S. Cities: A Machine-Learning Study on Meetup Data


  • CHRISTOPHER ARRAYA Aspiring Scientists' Summer Internship Program Intern
  • Olga Gkountouna Aspiring Scientists' Summer Internship Program Co-mentor
  • Myeong Lee Aspiring Scientists' Summer Internship Program Mentor
  • Ron Mahabir Aspiring Scientists' Summer Internship Program Mentor



Local groups such as those who organize neighborhood gatherings are an important pillar of modern society. Their presence and the successful engagement among members brings with various benefits, including making communities more cohesive, empowering community members , and promoting sustainable decisions that drive social transformation. We study various local group variables to understand what factors make some communities more successful than others. Data on US cities was collected from, an online event-based social networking platform that allows people to create and join online groups with the purpose of meeting offline in local communities. Both group level and event level factors were extracted from this data and augmented with community level variables. Several machine learning algorithms were then evaluated to assess the relationship between success factors and independent variables in order to identify those variables that are important for shaping the success of local groups. Our work has important implications for both local communities and governments. With respect to local communities, we provide a set of discernible measures that can be used to predict and improve the success of local community gatherings..For governments, an understanding of what drives local community gathering patterns will be an important knowledge base in making policies and planning community engagement strategies.





College of Science: Department of Computational and Data Sciences