Orion

Abstract

Social Networking Services (SNS) depend on user-generated content (UGC). A fraction of UGC is considered spam, such as adult, scam and abusive content. In order to maintain service reliability and avoid criminal activity, content moderation is employed to eliminate spam from SNS. Content moderation consists of manual content-monitoring operations and/or automatic spam-filtering. Detecting a small portion of spam among a large amount of UGC mostly relies on manual operation, thus it requires a large number of human operators and sometimes suffers from human error. In contrast, automatic spam-filtering can be processed with smaller cost, however it is difficult to follow spams' continuously changing trend, and it may declines service experience due to false positives. This presentation introduces an integrated content moderation platform called Orion'', which aims to minimize manual process and maximize detection of spam in UGC data. Orion preserves post history by users and services, which enables calculating the risk level of each user and decide whether monitoring is required. Also, Orion has a scalable API that can perform number of machine-learning based filtering processes, such as DNN (Deep Neural Network) and SVM for text and images that are posted in many SNS systems. We show that Orion improves efficiency of content moderation compared to a fully manual operation.