Aggression on social media has evolved into a major point of concern. However, recently proposed machine learning (ML) approaches to detect various types of aggressive behavior fall short, due to the fast and increasing pace of content gener- ation as well as evolution of such behavior over time. This work introduces the first, practical, real-time framework for detecting aggression on Twitter via embracing the streaming ML paradigm. This method adapts its ML binary classifiers in an

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... fashion, while receiving new annotated examples, and achieves similar performance as batch-based ML models, with 82–93% accuracy, precision, and recall. Experimental analysis on real Twitter data reveals how this framework, implemented in Spark Streaming, easily scales to process millions of tweets in minutes.