While the task of automatically recognising hateful content online has been extensively explored in the last years within the NLP community, what is the best strategy to respond to such messages has only recently entered the research agenda. One of the main issues related to this task is indeed how to best measure the effects of computer generated counter-narratives (i.e. textual responses to hate messages), in order to identify the most promising approaches. This thesis will explore this topic across NLP, NLG and complex networks in order to combine content-based, emotion-based and network-based metrics and apply them effectively to fight online hate via analysis of Social Media content spreading.