Social Media Content Ranking Based on Social Computing and User Influence

In this paper an innovative social media content ranking scheme is proposed. The proposed unsupervised architecture takes into consideration user-content interactions, since social media posts receive likes, comments and shares from friends and other users. Additionally the influence of each user is modeled, based on the centrality theory. Towards this direction both the degree and Bonacich's centrality are estimated for each user. Finally, a novel content ranking component is introduced, which ranks posted items based on a social computing method, driven by the power and influence of social network users. Initial experiments on real life social networks content illustrate the promising performance of the proposed architecture. Additionally comparisons with random selection chronological ordering (RSPICO), random selection non-chronological ordering (RSPIn-CO) and “My Facebook Movie” algorithms are provided.