Rumors and misleading information propagation is one of the main problems in Online Social Networks (OSN) that do not have mature and detreministic solutions. This paper proposes a Colored Petri Net Model (CPNM) for modeling the solution of detecting and blocking misleading information propagation in OSNs. The methodology of the proposed is simulated and its effectiveness evaluated on two datasets consisting of 1003 and 863- newsworthy tweets respectively. Tweets in each dataset have been collected from two trending topics (#ISIS and #CharlieHebdo) in Twitter social network. According to Precision, Recall, and Accuracy metrics, the results confirmed the effectiveness of CPNM in detecting misleading newsworthy tweets compared with other mechanisms. In addition, verifying the Reachability property of CPNM proved that detecting and blocking misleading tweets are attainable states according to the firing life-cycle of tokens.