Debate on Online Social Networks at the Time of COVID-19: An Italian Case Study

TL;DR

This study examines how the COVID-19 lockdown in Italy affected social media interactions around influencers on Instagram and Facebook, revealing increased activity, changing engagement patterns, and shifting topics during the pandemic's first six months.

Contribution

It provides a large-scale quantitative analysis of social network engagement and sentiment changes during COVID-19 lockdown in Italy, with a focus on influencers and user comments.

Findings

Increased user activity during lockdown periods

Significant daily and weekly variation in engagement

Rapid rise and fall of pandemic-related topics

Abstract

The COVID-19 pandemic is not only having a heavy impact on healthcare but also changing people's habits and the society we live in. Countries such as Italy have enforced a total lockdown lasting several months, with most of the population forced to remain at home. During this time, online social networks, more than ever, have represented an alternative solution for social life, allowing users to interact and debate with each other. Hence, it is of paramount importance to understand the changing use of social networks brought about by the pandemic. In this paper, we analyze how the interaction patterns around popular influencers in Italy changed during the first six months of 2020, within Instagram and Facebook social networks. We collected a large dataset for this group of public figures, including more than 54 million comments on over 140 thousand posts for these months. We analyze and compare engagement on the posts of these influencers and provide quantitative figures for aggregated user activity. We further show the changes in the patterns of usage before and during the lockdown, which demonstrated a growth of activity and sizable daily and weekly variations. We also analyze the user sentiment through the psycholinguistic properties of comments, and the results testified the rapid boom and disappearance of topics related to the pandemic. To support further analyses, we release the anonymized dataset.