Non-pharmaceutical Interventions and the Infodemic on Twitter: Lessons Learned from Italy during the Covid-19 Pandemic

Abstract

The COVID-19 pandemic changed expectations for information dissemination and use around the globe, challenging accepted models of communications, leadership, and social systems. We explore how social media discourse about COVID-19 in Italy was affected by the rapid spread of the virus, and how themes in postings changed with the adoption of social distancing measures and non-pharmaceutical interventions (NPI). We used topic modeling and social network analysis to highlight critical dimensions of conversations around COVID-19: 1) topics in social media postings about the Coronavirus; 2) the scope and reach of social networks; and 3) changes in social media content as the nation moved from partial to full social distancing. Twitter messages sent in Italy between February 11th and March 10th, 2020. 74,306 Tweets sent by institutions, news sources, elected officials, scientists and social media influencers. Messages were retweeted more than 1.2 million times globally. Non-parametric chi-square statistic with residual analysis to identify categories, chi-square test for linear trend, and Social Network Graphing. The first phase of the pandemic was dominated by social media influencers, followed by a focus on the economic consequences of the virus and placing blame on immigrants. As the crisis deepened, science-based themes began to predominate, with a focus on reducing the spread of the virus through physical distancing and business closures Our findings highlight the importance of messaging in social media in gaining the public’s trust and engagement during a pandemic. This requires credible scientific voices to garner public support for effective mitigation. Fighting the spread of an infectious disease goes hand in hand with stemming the dissemination of lies, bad science, and misdirection.

Using data mining to track the information spreading on social media about the COVID-19 outbreak

“…Using SNA as an analysis method can support emergency agencies in their understanding of the network structure of user interactions as situations rapidly emerge (Leon et al , 2020; Zhu and Wang, 2020). Using SNA to analyse the network on the COVID-19 pandemic includes COVID-19 transmission analysis based on the citizen interaction community network (Jo et al , 2021; Obadimu et al , 2021; Yie et al , 2021) and information spreading on social media (Ahmed et al , 2020; Al-Shargabi and Selmi, 2021; Bahja and Safdar, 2020; Gruzd and Mai, 2020; Massaro et al , 2021). Studies applied SNA on the topic of COVID-19 mainly focused on the centrality degree (betweenness centrality, closeness centrality, eigenvector centrality), PageRank, cohesive subgroups and core periphery of the networks to detect key nodes in the networks.…”

Making sense of COVID-19 over time in New Zealand: Assessing the public conversation using Twitter

“…As a result, in the last several years, the use of big data analytics to make sense of the tremendous volume of first-hand user-generated information accumulated on social media is becoming increasingly popular [e.g., 3 , 16 , 17 – 19 ]. Amongst social media platforms, Twitter has been the platform of most interest to researchers [ 20 ].…”

“…For instance, the focus of Lyu and Luli [ 22 ] and Li et al [ 24 ] was on the United States where the former extracted the topics and overarching themes emerging from the public COVID-related tweet discussion about the Centers for Disease Control and Prevention, and the latter used Twitter posts to detect stress symptoms related to COVID-19 in the US. Others have studied Italy [ 17 ], Brazil [ 25 ], China [ 26 ], the Arab region [ 27 ], and so on (See Table 1 ). Given the domestic focus of these studies, many have investigated languages other than English, for example Alshalan, et al [ 27 ] examined around 14 million Arabic tweets and identified fifteen public concerns and government pandemic measures as well as six macro-concerns.…”

Surveillance of Outbreaks of SARS-CoV-2 Infections at School in the Veneto Region: Methods and Results of the Public Health Response during the Second and Third Waves of the Pandemic between January and June 2021

“…The NPIs adopted also included early case isolation, social distancing, using face masks, and closing schools and businesses. These strategies limited virus-related mortality, and prevented local healthcare systems from being overwhelmed [2,3].…”

“…The surveillance systems began collecting records on January 4, but schools only reopened on 7 January 2021 2. From March 10, attendance was suspended locally for all high schools and the second and third years of middle schools (for 12 and 13-year-olds) if the local cumulative weekly incidence of infections exceeded 250 per 100,000 population, in accordance with national legislation.…”