A new paradigm of Internet of Things (IoT) is emerging rapidly by socializing the smarter physical devices called as Social Internet of Things (SIoT). Social relationships established between these objects make them autonomously connected for services, without any human intervention. Since SIoT is a large-scale network with huge data involved, the content spreading behaviour need to be exploited. In order to ensure the growth of the content spread, the large-scale SIoT network is divided into several communities based on the social attributes in this work. We first divided the SIoT network into high quality Sociality based Weighted Communities (SWC). Social attributes like user preferences, social similarities, and mutual friends’ degrees are main metrics for achieving the best rate function. The weighted method based on these social attributes determine the nodes to be present in their respective communities. Also, the controlling of the local community augmentation using cluster concepts is done in our approach. Finally, a Credential Acclaimed Information Spreading (CAIS) mechanism is proposed which selects the best node with the maximum credential to surge the content spreading behaviour in the detected communities of SIoT network. The proposed social-driven attribute based weighted mechanism for community detection is validated using three diverse real-world datasets such as CASAS, MIT and ARAS dataset containing 427 sensors. Investigational outcomes validate that the overall performance of the proposed method overwhelms the conventional community detection algorithms like Louvain, Girvan Newman, Bron Kerbosch, Infomax and the recent state-of-art-approaches interms of spreading outcomes, NMI, modularity, F-measure, precision, recall and computational time.