Linking Stance and Stereotypes About Migrants in Italian Fake News

This paper investigates stance and stereotypes within a dataset of Twitter conversational threads in Italian. The starting point of these conversations are tweets containing misinformation, in the form of racial hoaxes targeted at migrants, identified as untrustworthy by fake news debunking websites. The conversational structure of the dataset gives us the opportunity to observe and collect evidence about some linguistic and social phenomena at play in the propagation of stereotypes and the interactions between users which stem from them. We propose a theoretical background, as well as quantitative and qualitative analyses of our annotated data, at different levels of granularity, which can provide insights into the dynamics of Italian online discourses on the topic of migration.