Measuring Network Polarization and Political Sectarianism During the 2020 Pandemic

Online social networks are at the limelight of the public debate, where antagonistic groups compete to impose conflicting narratives and polarize the discussions. This article proposes an approach for measuring network polarization and political sectarianism in Twitter based on user interaction networks. Centrality metrics identify a small group of influential users (polarizers and unpolarizers) who influence a larger group of users (polarizees and unpolarizees) according to their ideological stance (left, right, and undefined). This network polarization is computed by the Bayesian probability using typical actions such as following, tweeting, retweeting, and replying. The measurement of political sectarianism also uses Bayesian probability and words extracted from the tweets to quantify the intensity of othering, aversion, and moralization in the debate. We collected Twitter data from 33 conflicted political events in Brazil during 2020, strongly influenced by the COVID-19 pandemic. Based on our methodology and polarization score, our results reveal that the approach based on user interaction networks leads to an increasing understanding of polarized conflicts in Twitter. Also, a small number of polarizers is enough to represent the polarization and sectarianism of Twitter events. © 2014 IEEE.

Less About Privacy: Revisiting a Survey about the German COVID-19 Contact Tracing App

The release of COVID-19 contact tracing apps was accompanied by a heated public debate with much focus on privacy concerns, e.g., possible government surveillance. Many papers studied people's intended behavior to research potential features and uptake of the apps. Studies in Germany conducted before the app's release, such as that by Häring et al., showed that privacy was an important factor in the intention to install the app. We conducted a follow-up study two months post-release to investigate the intention-behavior-gap, see how attitudes changed after the release, and capture reported behavior. Analyzing a quota sample (n=837) for Germany, we found that fewer participants mentioned privacy concerns post-release, whereas utility now plays a greater role. We provide further evidence that the results of intention-based studies should be handled with care when used for prediction purposes. © 2023 ACM.

Coordinated inauthentic behavior: An innovative manipulation tactic to amplify COVID-19 anti-vaccine communication outreach via social media.

Coordinated inauthentic behavior (CIB) is a manipulative communication tactic that uses a mix of authentic, fake, and duplicated social media accounts to operate as an adversarial network (AN) across multiple social media platforms. The article aims to clarify how CIB's emerging communication tactic "secretly" exploits technology to massively harass, harm, or mislead the online debate around crucial issues for society, like the COVID-19 vaccination. CIB's manipulative operations could be one of the greatest threats to freedom of expression and democracy in our society. CIB campaigns mislead others by acting with pre-arranged exceptional similarity and "secret" operations. Previous theoretical frameworks failed to evaluate the role of CIB on vaccination attitudes and behavior. In light of recent international and interdisciplinary CIB research, this study critically analyzes the case of a COVID-19 anti-vaccine adversarial network removed from Meta at the end of 2021 for brigading. A violent and harmful attempt to tactically manipulate the COVID-19 vaccine debate in Italy, France, and Germany. The following focal issues are discussed: (1) CIB manipulative operations, (2) their extensions, and (3) challenges in CIB's identification. The article shows that CIB acts in three domains: (i) structuring inauthentic online communities, (ii) exploiting social media technology, and (iii) deceiving algorithms to extend communication outreach to unaware social media users, a matter of concern for the general audience of CIB-illiterates. Upcoming threats, open issues, and future research directions are discussed.

The "Green Pass" Controversy in the Italian Twittersphere: a Digital Methods Mapping

In this paper we developed a digital methods mapping of the controversy arises from the adoption of the so-called "Green Pass" in Italy Adopting an "agnostic" approach to our object of study, we used a well-established research design: namely, to collect all the tweets that contain words related to conversations about the green pass in Italy (e.g.: green pass, #greenpass). In this way, the sample collected amounts to 4.307.487 tweets, published between June 15, 2021, and December 15, 2021. To bring out the "voices" of the different actors involved in the controversy we adopted a quali-quantitative approach: on the one hand, by means of computational techniques, we reconstructed the structural relations in which the actors are involved and its evolution over time;on the other hand, by means of content analysis we enriched our map with an interpretation of the discourse surrounding the controversy. Finally, these cartographic results are discussed considering the Italian media system functioning, in order to understand how its conformation may have influenced the public debate concerning the green pass.

#Covid-19: A hashtag for examining reactions towards Europe in times of crisis. An analysis of tweets in Italian, Spanish, and French

Hashtag research has established itself as a relevant research field, with various studies having analysed this polysemic collector in crisis and media events. Hashtags are used in social media, most specifically on Twitter. Further, between 2020 and 2021, hashtag studies linked to the COVID-19 pandemic have emerged. Accordingly, this study aimed to analyse the content of tweets during the first phase of the COVID-19 pandemic (March 4-11, 2020) that included the hashtag #Covid-19 in three different languages: Italian, Spanish, and French. For these analyses, we used emotional text mining. The goal of this study was to reconstruct the representation of the pandemic, of containment measures, and of Europe in tweets. We discussed the prevailing attitude towards Europe in times of crisis.

Continuous EMF Monitoring as an Emergency and Disaster Detection Tool

A number of applications founded on electromagnetic field (EMF) has increased, since wireless personal communication devices are used by a large number of people. Simultaneously, the controversy about adverse health effects of EMF exposure is in a focus of the public debate. Thus, there are constant demands for comprehensive investigation and monitoring of existing exposure to EMF. In last decade, the wireless sensors networks emerged as an innovative solution for EMF monitoring in the environment. The newest established is Serbian EMF RATEL network that performs continuous wideband monitoring, counting contribution of all active EMF sources, in particular frequency range and in the vicinity of observed location. Besides the monitoring for the health protection purposes, this network can be used as an emergency and disaster detection tool, as demonstrated in a case study of COVID-19 presence in campus of the University of Novi Sad. In this paper, technical details of the Serbian EMF RATEL monitoring network are presented, along with monitoring results from two campus locations, which clearly indicate some significant changes in the EMF level. © 2022 IEEE.

A Privacy Calculus Model for Contact Tracing Apps: Analyzing the German Corona-Warn-App

The SARS-CoV-2 pandemic is a pressing societal issue today. The German government promotes a contact tracing app named Corona-Warn-App (CWA), aiming to change citizens’ health behavior during the pandemic by raising awareness about potential infections and enable infection chain tracking. Technical implementations, citizens’ perceptions, and public debates around apps differ between countries, i.e., in Germany there has been a huge discussion on potential privacy issues of the app. Thus, we analyze effects of privacy concerns regarding the CWA, perceived CWA benefits, and trust in the German healthcare system to answer why citizens use the CWA. We use a sample with 1,752 actual users and non-users and find support for the privacy calculus theory, i.e., individuals weigh privacy concerns and benefits in their use decision. Thus, citizens’ privacy perceptions about health technologies (e.g., shaped by public debates) are crucial as they can hinder adoption and negatively affect future fights against pandemics. © 2022, IFIP International Federation for Information Processing.

The Construction of Communicative Space: The Nature of COVID-19 Information in Italy

The monopolistic role played by the pandemic offers a lens through which we can observe how the digital environment is reshaping the entire communicative space. Starting from Bourdieu’s concept of the “journalistic field”, what we now have is a situation in which the voices of journalists and of the public sector’s communicators are flanked by those of several other actors, who together are creating a polyphonic, multidimensional communicative space. Thanks to their communicative resources, they take on a unique role and position in the communicative space. Based on these premises, the present work aims to empirically explore how 10 different main actors within the sphere of public debate managed their communication strategies on Facebook during the Italian lockdown. In order to test the theoretical framework proposed here, we analyze 6 different kinds of communicative resource: reputational, institutional, professional, argumentative, performative and relational ones. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

COVID-19 Vaccine Discussion: Evidence from Twitter Data Using Text Mining

COVID-19 vaccination has led to unrest within societies, and intense public debates are often carried out on social media platforms like Twitter. A better understanding of concerns, issues, and communication on COVID-19 vaccines is a first step to reducing tension within society and improving the negative effects of the pandemic. It can also contribute to addressing the concerns of advocates and opponents, which is essential in the battle against this and possible future pandemics. At the same time, many people report pressure to undergo vaccination in order to continue participating in social and professional life. COVID-19 vaccination has triggered a complex discussion among the public. We use text mining algorithms suitable for big datasets to identify relevant categories of discourse and sentiments from about 250,000 tweets. Our findings highlight (and quantify) expressed shortcomings in vaccination programs related to administration, planning, information, and protective measures. It also hints that rare and severe incidents related to vaccination have a more substantial impact than potential fears related to non-familiar technology such as "mRNA"causing uncertainty. We also provide an extensive discussion setting forth suggestions that might help deal with the current and future pandemic. © 2021 ACM.

An analysis of Twitter users opinions on vaccines using Machine Learning techniques

Vaccines are an old technique, known and used for over 200 years. However, it is likely that the arrival of the COVID-19 pandemic made the public debate around this technology become polarized at a level never seen before. Thus, this work aims to determine and understand factors that lead Brazilian users on Twitter to be favorable or not to vaccines by first determining users' stances in relation to the vaccination topic and then using Machine Learning methods to infer demographic information and determine which are the socio-demographic factors that cause the greatest impact on users' opinions on vaccines. First, a data set composed of relevant demographic information from users who stand for or against vaccines was generated. Then, from the collected data, charts were generated showing the distributions of the obtained demographic information and Machine Learning algorithms were applied to the data set in order to generate relevant models for the research. Finally, the information collected in the previous steps was analyzed in order to draw relevant conclusions about how each demographic factor considered influences the formation of Twitter users opinions on vaccines and their use. The methodology proposed produced informative and pertinent results, and it was possible to determine that age and location are the factors that cause the most significant influence on users' opinions. Our work proposes an efficient and agile framework that can be easily and readily implemented and extended to understand not only stances on vaccines, but also opinions on any subject of public debate. © 2021 IEEE.