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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 andoi:10.5281/zenodo.4720556 fatcat:z76wzfmwa5c5fe2j6oxyeh2cai