The integral presence of social networks in users’ lives, has made them the main means for information dissemination. A comprehensive analysis of users’ behaviors plays a pivotal role in discerning information diffusion patterns. We extract an insightful model of users’ behaviors using both automata and machine learning techniques by observing users’ activities within the social network. Assuming users with similar characteristics show similar behavior on a topic, we cluster the users triggering activities related to a topic based on effective behavior factors identified within a social network using the KMeans algorithm. To handle the large number of user activities and make the active automata learning technique feasible, we abstract the user of each activity by its cluster. We extract a model for users’ behavior in the form of a deterministic finite automaton by employing the L \(^*\) and KV algorithms with an oracle tailored by a tree organization of sequences combined with a one-class SVM classifier. We illustrate the applicability of our approach on two datasets obtained from Twitter on COVID-19 topics. The extracted models have around \(65\%\) to \(75\%\) accuracy.