A distributed streaming framework for connection discovery using shared videos

With the advances in mobile devices and the popularity of social networks, users can share multimedia content anytime, anywhere. One of the most important types of emerging content is video, which is commonly shared on platforms such as Instagram and Facebook. User connections, which indicate whether two users are follower/followee or have the same interests, are essential to improve services and information relevant to users for many social media applications. But they are normally hidden due to users' privacy concerns or are kept confidential by social media sites. Using user-shared content is an alternative way to discover user connections. This article proposes to use user-shared videos for connection discovery with the Bag of Feature Tagging method and proposes a distributed streaming computation framework to facilitate the analytics. Exploiting the uniqueness of shared videos, the proposed framework is divided into Streaming processing and Online and Offline Computation. With experiments using a dataset from Twitter, it has been proved that the proposed method using user-shared videos for connection discovery is feasible. And the proposed computation framework significantly accelerates the analytics, reducing the processing time to only 32% for follower/followee recommendation. It has also been proved that comparable performance can be achieved with only partial data for each video and leads to more efficient computation.