  On social media platforms, the act of predicting reposting is seen as a
challenging issue related to Short Message Services (SMS). This study examines
the issue of predicting picture reposting in SMS and forecasts users' behavior
in sharing photographs on Twitter. Several research vary. The paper introduces
a network called Image Retweet Modeling (IRM) that models heterogeneous image
retransmission. It considers the user's previous reposting of the image tweet,
the next contact in the SMS, and the preferences of the reposted person. Three
aspects connected to content. A text-guided multimodal neural network is
developed to create a novel multi-faceted attention ranking network
methodology. This allows for learning the joint image Twitter representation
and user preference representation in the prediction job. Multiple experiments
conducted on extensive data sets demonstrate that our approach outperforms
current methods on Social Network platforms.
