An online and highly-scalable streaming platform for filtering trolls with transfer learning

The internet has reached a mature stage of development, and Online Social Media (OSM) platforms such as Twitter and Facebook have become vital channels for public communication and discussion on matters of public interest. However, these platforms are often plagued by improper statements or content, propagated by anonymous users and trolls, which negatively impact both the platforms and their users. Existing methods for dealing with inappropriate information rely on (semi)-manual offline assessments, which do not fully account for the streaming nature of OSM feeds. In this paper, we implement a robust and decoupled system that considers social media data as streaming data. With a publisher and consumer model, our system can process more than 179 MB of data per second with only 166.3 ms latency using Apache Kafka. Accordingly, we deploy a well-trained transfer learning model to classify incoming data streams, with an accuracy of 0.836. Our proposed architecture has the potential to assist online communities in developing more constructive and flawless OSM platforms. We believe that our contribution will help address the challenges associated with improper content on OSM platforms and pave the way for the development of more effective and efficient solutions.