Characterizing user connections in social media through user shared image

Author: 

Cheung, M
She, J
Wang, N

Publication Date: 

13 October 2017

Journal: 

IEEE Transactions on Big Data

Last Updated: 

2020-05-20T22:47:30.04+01:00

Issue: 

4

Volume: 

4

DOI: 

10.1109/TBDATA.2017.2762719

page: 

447-458

abstract: 

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.

Symplectic id: 

740634

Submitted to ORA: 

Submitted

Publication Type: 

Journal Article