Italian Twitter semantic network during the Covid-19 epidemic

TL;DR

This study analyzes Twitter's semantic network during Italy's COVID-19 lockdown to understand how misinformation spreads across different political communities and the topics they discuss.

Contribution

It provides a novel analysis of Italian Twitter discourse during COVID-19, highlighting how misinformation is unevenly distributed among political communities.

Findings

Discursive communities largely align with traditional political parties.

Misinformation topics are a minority but vary in popularity across communities.

Semantic networks reveal community-specific exposure to misinformation.

Abstract

The Covid-19 pandemic has had a deep impact on the lives of the entire world population, inducing a participated societal debate. As in other contexts, the debate has been the subject of several d/misinformation campaigns; in a quite unprecedented fashion, however, the presence of false information has seriously put at risk the public health. In this sense, detecting the presence of malicious narratives and identifying the kinds of users that are more prone to spread them represent the first step to limit the persistence of the former ones. In the present paper we analyse the semantic network observed on Twitter during the first Italian lockdown (induced by the hashtags contained in approximately 1.5 millions tweets published between the 23rd of March 2020 and the 23rd of April 2020) and study the extent to which various discursive communities are exposed to d/misinformation arguments. As observed in other studies, the recovered discursive communities largely overlap with traditional political parties, even if the debated topics concern different facets of the management of the pandemic. Although the themes directly related to d/misinformation are a minority of those discussed within our semantic networks, their popularity is unevenly distributed among the various discursive communities.