Clustering the images shared through social network (SN) platforms according to theacquisition cameras embedded in smartphones is regarded as a significant task in forensic investigationsof cybercrimes. The sensor pattern noise (SPN) caused by the camera sensor imperfections during themanufacturing process can be extracted from the images and used to fingerprint the smartphones. The processof content compression performed by the SNs causes loss of image details and weakens the SPN, makingthe clustering task even more challenging. In this paper, we present a hybrid algorithm capable of clusteringthe images captured and shared through SNs without prior knowledge about the types and number of theacquisition smartphones. The hybrid method exploits batch partitioning, image resizing, hierarchical andgraph-based clustering approaches to cluster the images. Using Markov clustering, the hierarchical clusteringis conducted in such a way that the representative clusters with a higher probability of belonging to the samecamera are selected for merging, which accelerates the clustering. For merging the clusters, the adaptivethreshold updated iteratively through the hybrid clustering is used, which results in more precise clusterseven for images from the same model of smartphones. The results on the VISION dataset, including bothnativeandshared images, prove the effectiveness and efficiency of the hybrid method in comparison withthe state-of-the-art SPN-based image clustering algorithms.