Neural Networks Based Automatic Content Moderation on Social Media

Abstract

Millions of users produce and consume billions of content on social media. Therefore, humanreviewed content moderation is not achievable in such volume. Automating content moderation is a scalable solution for social media platforms. In this thesis work, we propose a neural networks based automatic content moderation pipeline. Our solution consists of two main parts: the first part that classifies the content into granular content classes and a second part that automatically obfuscates the part of the image that might be inappropriate for the target audience. The proposed solution is cost-efficient in terms of human labour. Our classification network is trained with automatically labelled data using noise-robust techniques. Our automatic obfuscation algorithm uses the information obtained from the classification network and does not require additional annotation or supplementary training. This obfuscation algorithm presents a novel-use case to the state-of-the-art.