Collaborative analysis model for trending images on social networks

The ubiquitous existence of personal mobile devices and the emergence of online social media have attracted an increasing number of users. This increase is due to the active presence of social media in these mobile devices. The convergence of social media and mobile communication networks generates a considerable amount of trending media contents (e.g., texts, audio, videos, and images) on the Internet while exchanging or sharing contents. However, while exchanging messages or trending contents through social networks, there is a serious concern to identify whether the contents or the images are real or belong to the claimed context using associated descriptive tags. In this paper, we propose a collaborative analysis model to identify if any trending image is altered or modified, and whether the content used to describe it is actually accurate. It is a challenge to provide the desired trending media content in a short time from the vast amount of trending images on the social networks. Online social network environment is heterogeneous in nature, which demands computational Intelligent (CI) techniques to deliver the required media content in an acceptable time to the users. To this end, a collaborative search algorithm is used to utilize the descriptive tags, and user interaction history. The collaboration of tags powers the investigation of a trending images. Accordingly, we demonstrate the potential of the proposed model by performing experiments on the online collected data set using the proposed collaborative analysis model.