Driving Factors of Polarization on Twitter During Protests Against COVID-19 Mitigation Measures in Vienna

We conduct the analysis of the Twitter discourse related to the anti-lockdown and anti-vaccination protests during the so-called 4th wave of COVID-19 infections in Austria (particularly in Vienna). We focus on predicting users' protest activity by leveraging machine learning methods and individual driving factors such as language features of users supporting/opposing Corona protests. For evaluation of our methods we utilize novel datasets, collected from discussions about a series of protests on Twitter (40488 tweets related to 20.11.2021;7639 from 15.01.2022 – the two biggest protests as well as 192 from 22.01.2022;8412 from 11.12.2021;3945 from 11.02.2022). We clustered users via the Louvain community detection algorithm on a retweet network into pro- and anti-protest classes. We show that the number of users engaged in the discourse and the share of users classified as pro-protest are decreasing with time. We have created language-based classifiers for single tweets of the two protest sides – random forest, neural networks and a regression-based approach. To gain insights into language-related differences between clusters we also investigated variable importance for a word-list-based modeling approach. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Access to healthcare as an important moderating variable for understanding the geography of COVID-19 outcomes - preliminary insights from Poland

Introduction: Biases in the measurement of COVID-19 burden and the uncertainty in estimation of the corresponding epidemiologic indexes are known and common phenomena in infectious diseases. We investigated to what extent healthcare access (HCA)-related supply/demand interfered with the registered data on COVID-19 in Poland. Material and Methods: We ran a multiple linear regression model with interactions to explain the geographic variation in seroprevalence, hospitalizations (on the voivodeship - NUTS-2 level) and current (beginning of the 4th wave of COVID cases - 15.09-21.11.2021) case notifications/crude mortality (on poviat - old NUTS-4 level). We took vaccination coverage and cumulative case notifications up to the so called 3rd wave as predictor variables and supply/demand (HCA) as moderating variables. Results: HCA with interacting terms (mainly demand) explained to the great extent the variance of current incidence and most of the variance in the current mortality rates. HCA (mainly supply) was significantly moderating cumulative case notifications until the 3rd wave of cases, thus explaining the variance in seroprevalence and hospitalization. Conclusion: Seeking causal relations between the vaccination- or infection-gained immunity level and the current infection dynamics could be misleading without understanding the socio-epidemiologic context such as the moderating role of HCA (sensu lato). After quantification, HCA could be incorporated into epidemiologic models for improved prediction of the actual disease burden. Copyright © Medical University of Gdańsk.

Social Cohesion During the Stay-at-Home Phase of the First Wave of the COVID-19 Pandemic on Polish-Speaking Twitter

Catastrophic and urgent events, such as the COVID-19 pandemic, are known not only to polarize societies and induce selfish, individualistic behavior, but might also motivate altruistic behavior. We have analyzed COVID-19 perception using data collected from the Polish-language Internet from 15.01-30.06.2020, equaling 930,319 tweets. Deploying methods of computational social science and digital epidemiology, we aim to understand mechanisms of social consolidation and depolarization (measured by network modularity and sentiment) during the so-called “stay-at-home phase” of the COVID-19 pandemic. Mauss’ theory of interaction or exchange of gifts, the theory of social capital, as well as Kaniasty’s theory of mobilization and deterioration serve as a background for reflection on the Polish example during the first epidemic wave. Our study highlights the potential of social support and caretaking to reduce affective and behavioral polarization in social media © 2021, Springer Nature Switzerland AG.

Protest Perspective Against COVID-19 Risk Mitigation Strategies on the German Internet

The aim of this study is to quantitatively assess perception of protests around COVID-19 in Germany from the late July till the end of August 2020 in the Internet media by infodemiological approach. To this end we investigate Google searches, Twitter and Telegram posts, and selection of news articles via EventRegistry. We focus on narratives around Berlin Demonstrations on August 1st and August 29th, 2020. Using media intelligence we spot trends, analyze relevant topics over a longer time span and create sociolinguistic landscapes targeting Querdenken and QAnon movements and other actors such as AfD, SPD, and Green political parties and Antifa. Although the dominant actors of the protest are on the far-right political spectrum, we demonstrate (based on network analysis) that left-wing activists could both sympathize with (e.g. some liberal greens) and oppose (e.g. Antifa) the protest. Although we observe a constant interest in the protest movements in traditional media, their popularity on social media is growing (for Querdenken faster than for QAnon). The revealed insights shed light on social dynamics in the context of such major disruptive events as COVID-19 pandemic and could serve as a basis for optimization of risk awareness campaigns by the authorities. © 2020, Springer Nature Switzerland AG.