Over the years, there has been growing concern about the disproportionate use of hate speech on social media platforms. In this paper, we present a text analysis for detecting abusive language in Italian messages on Facebook, surrounding the debate over the migrant-rescue ship, Sea Watch 3, and its captain Carola Rackete. The study data consists of more than 130,000 posts retrieved from two pages relating to Matteo Salvini, the leader of the Italian Lega political party, and from the official Facebook pages of five Italian newspapers. To explore the presence of offensive and hatred expressions in the corpus and to establish to what extent social users’ language differs, depending on the type of Facebook pages analysed, we ran a topic model based on Latent Dirichlet Allocation. We have complemented this approach with tools from semantic network analysis
Facebook debate on Sea Watch 3 case: detecting offensive language through Automatic Topic Mining Techniques
Emiliano del Gobbo;
2020-01-01
Abstract
Over the years, there has been growing concern about the disproportionate use of hate speech on social media platforms. In this paper, we present a text analysis for detecting abusive language in Italian messages on Facebook, surrounding the debate over the migrant-rescue ship, Sea Watch 3, and its captain Carola Rackete. The study data consists of more than 130,000 posts retrieved from two pages relating to Matteo Salvini, the leader of the Italian Lega political party, and from the official Facebook pages of five Italian newspapers. To explore the presence of offensive and hatred expressions in the corpus and to establish to what extent social users’ language differs, depending on the type of Facebook pages analysed, we ran a topic model based on Latent Dirichlet Allocation. We have complemented this approach with tools from semantic network analysisI documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
