Cyberbullying and cyberaggression are serious and widespread issues increasingly affecting Internet users. With the “help” of the widespread of social media networks, bullying once limited to particular places or times of the day, can now occur anytime and anywhere. Cyberaggression refers to aggressive online behaviour intending to cause harm to another person, involving rude, insulting, offensive, teasing or demoralising comments through online social media. Considering the gravity of the consequences that cyberaggression has on its victims and its rapid spread amongst internet users (specially kids and teens), there is an imperious need for research aiming at understanding how cyberbullying occurs, in order to prevent it from escalating. Given the massive information overload on the Web, it is crucial to develop intelligent techniques to automatically detect harmful content, which would allow the large-scale social media monitoring and early detection of undesired situations. Considering the challenges posed by the characteristics of social media content and the cyberaggression task, this paper focuses on the detection of aggressive content in the context of multiple social media sites by exploring diverse types of features. Experimental evaluation conducted on two real-world social media dataset showed the difficulty of the task, confirming the limitations of traditionally used features.