Analysing the effect of counter-narratives on hateful conversations online
Nicolò Penzo (Ph.D. Student)
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. Specifically, some works in the last few years have tackled the issue of automatically generating counter-narratives (i.e. textual responses to hate messages), to combat online hatred in a sound, well-grounded and polite way. However, one of themain issues related to this task is how to best measure the effects of computer-generated counter-narratives. In fact, being able to assess the impact of counter-speech would allow researchers to identify the most promising approaches and contribute the design of a common evaluation framework. 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.
|Primary Host:||Bruno Lepri (FBK & MIT Media Lab)|
|Exchange Host:||Goran Glavaš (University of Würzburg)|
|PhD Duration:||01 November 2022 - 31 October 2025|
|Exchange Duration:||01 January 2024 - 30 June 2024 - Ongoing|