Most previous works in privacy of Online Social Networks (OSN) focus on a restricted scenario of using one type of information to infer another type of information or using only static profile data such as username, profile picture or home location. However the multimedia footprints of users has become extremely diverse nowadays. In reality, an adversary would exploit all types of information obtainable over time, to achieve its goal. In this paper, we analyse OSN privacy by jointly exploiting longterm multimodal information. We focus in particular on inference of social relationships. We consider five popular components of posts shared by users, namely images, hashtags, captions, geo-locations and published friendships. Large scale evaluation on a real-world OSN dataset shows that while our monomodal attacks achieve strong predictions, our multimodal attack leads to a stronger performance with AUC (area under the ROC curve) above 0.9. Our results highlight the need for multimodal obfuscation approaches towards protecting privacy in an era where multimedia footprints of users get increasingly diverse.