Empowering First Responders through Automated Multimodal Content Moderation

Social media enables users to spread information and opinions, including in times of crisis events such as riots, protests or uprisings. Sensitive event-related content can lead to repercussions in the real world. Therefore it is crucial for first responders, such as law enforcement agencies, to have ready access, and the ability to monitor the propagation of such content. Obstacles to easy access include a lack of automatic moderation tools targeted for first responders. Efforts are further complicated by the multimodal nature of content which may have either textual and pictorial aspects. In this work, as a means of providing intelligence to first responders, we investigate automatic moderation of sensitive event-related content across the two modalities by exploiting recent advances in Deep Neural Networks (DNN). We use a combination of image classification with Convolutional Neural Networks (CNN) and text classification with Recurrent Neural Networks (RNN). Our multilevel content classifier is obtained by fusing the image classifier and the text classifier. We utilize feature engineering for preprocessing but bypass it during classification due to our use of DNNs while achieving coverage by leveraging community guidelines. Our approach maintains a low false positive rate and high precision by learning from a weakly labeled dataset and then, by learning from an expert annotated dataset. We evaluate our system both quantitatively and qualitatively to gain a deeper understanding of its functioning. Finally, we benchmark our technique with current approaches to combating sensitive content and find that our system outperforms by 16% in accuracy.