Event-based online social networks, which are used to maintain interest-based groups and to distribute and organize offline events, have recently gained increasing popularity. In event-based social networks, some groups survive and thrive, while other groups fail. How to build successful groups and what factors make a 'healthy' group are important open problems. We address the problem of modeling social group behavior and present detailed studies on group failure prediction by analyzing a large online event-based social network. We investigate both the statistical properties and the structural features of the social groups, and find that event features play an important role in distinguishing social groups with different topics and categories. We also observe that tightly knit communities have less average event participation, and both low level diversity and high level diversity in members' event participation will harm group activity participation. We then analyze the data of thousands of social groups collected from the Meetup platform with the goal of understanding what makes a group fail. We use two different feature selection methods in this paper and build a model to predict which groups will fail over a period of time. The experimental results show that social group failures can be predicted with high accuracy, and that member features contribute significantly to the success of social groups.