Doctoral Meeting: 'Hyperpartisan News Detection'

With the rise of digital technologies, social networks have become the primary source of information for many people. However, these platforms are increasingly contaminated with misinformation and extremist content, contributing to political polarization. This research analyzes the structure of such content to understand how partisan media communicate with audiences and reinforce polarization in democratic societies. The focus lies in exploring novel approaches to detect hyperpartisan news in Europe using hybrid methods—ranging from the computational modeling of linguistic features to more technical tools such as Large Language Models.

• Supervisors: Pablo Gamallo and Gaël Dias
• Moderator: Daniel Veira