Characterizing user connections in social media through user shared image

Billions of user images, which are shared on social media, can be widely accessible by others due to their sharing nature. Using machine-generated labels to annotate those images is a reliable for user connections discovery on social networks. The machine-generated labels are obtained from encoded vectors using up-to-date image processing and computer vision techniques, such as convolution neural network. By analyzing 2 million user-shared images from 8 online social networks, a phenomenon is observed that the distribution of user similarity based on their shared images follows exponential functions. Users who share visually similar images are likely having follower/followee relationships, regardless of the origins and the content sharing mechanisms of a social network. This phenomenon is nicely formulated for a multimedia big data recommendation engine as an alternative to social graphs for recommendation. By utilizing the formulation of the distribution, it is proven the proposed engine can be 46% better than previous approaches in F1 score and achieves a comparable performance of friends-of-friends approach. To the best of our knowledge, this is the first attempt in related fields to characterize such phenomenon by massive user-shared images collected from real-world SNs, and then formulate into practical analytics engine for connection discovery.