Vignesh Raja

Acting on policy-violating content as quickly as possible is a top priority of Reddit’s Safety team and is accomplished through technologies such as Rule-Executor-V2 (REV2), a real-time rules-engine that processes streams of events flowing through Reddit. While a low time-to-process latency, measured as the time it takes for some activity on the site to flow through REV2, is an important metric to optimize for, it is equally important for REV2 to be able to identify more sophisticated policy-violating content. At Reddit, we use advanced machine learning (ML) signals to detect and action such content. In this talk, we will discuss Signals-Joiner, a Flink-based system, which enables REV2 to leverage slower-to-compute ML signals in its real-time context via stream joins. Specifically, we'll walk through the motivation behind the system, the evolution of our architecture (building a custom windowing strategy), key learnings, and the results we've achieved.

Reddit brings community and belonging to over 52 million users everyday who post content and converse with one-another. With this scale, it is challenging to detect and act on policy-violating content in real time to minimize exposure of abusive content to users.

Reddit is home to more than 100,000 communities and 52 million daily active users. But it's not all cat videos and memes: Reddit's Anti-Evil teams fight spam, harassment and other threats every day.