Connection Discovery using Big Data of User Shared Images in Social Media Billions of user shared images are generated by individuals in many social networks today, and this particular form of user data is widely accessible to others due to the nature of online social sharing. When user social graphs are only accessible to exclusive parties, these user shared images are proved to be an easier and effective alternative to discover user connections. This work investigated over 360,000 user shared images from two social networks, Skyrock and 163 Weibo, in which 3 million follower/followee relationships are involved. It is observed that the shared images from users with a follower/followee relationship show relatively higher similarities. A multimedia big data system that utilizes this observed phenomenon is proposed as an alternative to user generated tags and social graphs for follower/followee recommendation and gender identification. To the best of our knowledge, this is the first attempt in this field to prove and formulate such a phenomenon for mass user shared images along with more practical prediction methods. These findings are useful for information or services recommendations in any social network with intensive image sharing, as well as for other interesting personalization applications, particularly when there is no access to those exclusive user social graphs.