Deep Learning and Adaptive Sharing for Online Social Networking
Prompted by Facebook Research's recent announcement on using deep learning to help users avoid 'drunk posting' embarrassing information on the social networking platform, I wrote an article for The Conversation about deep learning and adaptive sharing. This draws on our research on Adaptive Sharing for Online Social Networks, which was recognised as the Best Paper at the 13th IEEE International Conference on Trust, Security and Privacy in Computing and Communications (IEEE TrustCom-14). The following is a short excerpt from the article:
Facebook’s initial target appears aimed at extending its face recognition capability to automatically differentiate between a user’s face when sober and drunk, and use this to get a user to think twice before hitting the post button. Of course being detected as being drunk in photographs won’t be the only factor that determines when we want to moderate our social media sharing behaviours. The nature of the links we share, like and comment on can reveal a wealth of information about us, from ethnic and socio-economic background to political inclination and our sexuality. This makes the task for any artificial intelligence of managing our online privacy a challenging one.The full version of the article has been republished by WashingtonPost.com, Phys.org and several other sites. It can be read on The Conversation UK under the title 'Deep learning could prevent you from drunk posting to Facebook'.
A key challenge to help us manage our privacy more effectively will be to develop techniques that can analyse the data – photographs, their time and location, the people in them and how they appear, or the content of links – and correlate this to the privacy implications for the user given the privacy settings.
Our own research on adaptive sharing in social networks uses a quantitative model of privacy risk and social benefit to evaluate the effect of sharing any given piece of information with different members of the user’s social network. Then it can provide recommendations for audiences to share with, or avoid.
Like Facebook’s efforts, our work is to apply machine-learning techniques – which will one day include detecting drunkenness in photographs, or automatically determining the sensitivity of different information and calculating the potential regret factor of the post you’re about to make. Far from being a flippant or fanciful use of technology, these sorts of models will become a core part of the way we can engineer better privacy-awareness into the software we use.