The pervasive use of social media (e.g., Facebook, Stack Exchange, and Wikipedia) is providing unprecedented amounts of social data. Data mining techniques have been widely used to extract knowledge from such data, e.g., community detection and sentiment analysis. However, there is still much space to explore in terms of the event data (i.e., events with timestamps), such as posting a question, commenting on a tweet, and editing a Wikipedia article. These events reflect users’ behavior patterns and operational processes in the media sites. Classical process mining techniques support to discover insights from event data generated by structured business processes. However, they fail to deal with the social media data which are from more flexible ‘media’ processes and contain one-to-many and many-to-many relations. This paper employs a novel type of process mining techniques (based on object-centric behavioral constraint models) to derive insights from the event data in social media. Based on real-life data, process models are mined to describe users’ behavior patterns. Conformance and performance are analyzed to detect the deviations and bottlenecks in the question and answer process in the Stack Exchange website.