Funded by CONICET and Royal Society
It is common for online social media platforms to recommend content to its users through features such as “whom to follow” or personalised content. It has been shown however that social media platforms also spread harmful contents that have proven problematic. This project aims to detect and mitigate content flagged as online harm, which includes hate speech and misinformation. These problematic issues in social media have led to mental health issues owing to hate speech messages, as well as a disruption of the democratic system due to the diffusion of misinformation. Identification of harmful content online has however proven difficult, with not only the scientific community but also social media platforms and governments worldwide calling for support to develop effective methods.
To reduce the impact of harmful content online, this project aims to develop novel content recommendation algorithms which are aware of harmful content, hence promoting recommendation of safe content. The objectives of this project are twofold: (1) it will explore novel techniques for furthering methods for detecting misinformation and hate speech, where existing methods fail to generalise, and (2) it will propose novel approaches to recommender systems that incorporate an awareness of harmful content in social media.
PIs: Daniela Godoy (Argentina) - Arkaitz Zubiaga (UK)
Research Team:
Rabab Alkhalifa (UK)
Aiqi Jiang (UK)
Matthew Purver (UK)
Juan Manuel Rodriguez (Argentina)
Silvia Schiaffino (Argentina)
Antonela Tommasel (Argentina)