  The rise of online aggression on social media is evolving into a major point
of concern. Several machine and deep learning approaches have been proposed
recently for detecting various types of aggressive behavior. However, social
media are fast paced, generating an increasing amount of content, while
aggressive behavior evolves over time. In this work, we introduce the first,
practical, real-time framework for detecting aggression on Twitter via
embracing the streaming machine learning paradigm. Our method adapts its ML
classifiers in an incremental fashion as it receives new annotated examples and
is able to achieve the same (or even higher) performance as batch-based ML
models, with over 90% accuracy, precision, and recall. At the same time, our
experimental analysis on real Twitter data reveals how our framework can easily
scale to accommodate the entire Twitter Firehose (of 778 million tweets per
day) with only 3 commodity machines. Finally, we show that our framework is
general enough to detect other related behaviors such as sarcasm, racism, and
sexism in real time.
