ORIGINAL RESEARCH article
Front. Polit. Sci.
Sec. Political Participation
Volume 7 - 2025 | doi: 10.3389/fpos.2025.1515923
This article is part of the Research TopicUnderstanding Noncompliance with COVID-19 Containment Measures: Factors, Manifestations and EffectsView all 5 articles
Perceived Oppression and Online Support for COVID-19 Noncompliance: The 2021 Trieste Port Protests
Provisionally accepted
- University of Padua, Padua, Italy
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The enforcement of COVID-19 containment measures, such as lockdowns or mandatory vaccination, can have significant consequences for both public health and the economic stability of institutions. As a result, widespread non-compliance poses a particular challenge for governments. In October 2021, workers in Trieste, Italy, blocked the city's commercial port in protest against new restrictions, triggering large-scale demonstrations and an explosion of related activity on Twitter. We analyze this unique case of mass non-compliance by collecting tweets about the protests and applying sentiment analysis, topic modeling, opinion analysis, user clustering, and diffusion modeling. Our findings reveal a strong connection between online support for non-compliance and emotions such as anger and mistrust, particularly toward the government, which are often framed as reactions to perceived injustice or oppression. The results suggest that social media played a key role in amplifying and normalizing non-compliant sentiment. While this study does not make normative claims about the legitimacy of either side, it offers a methodological lens for understanding how polarizing debates unfold online. This approach can also be extended to other divisive issues such as climate policy, military conflict, or artificial intelligence, or in general with regards to contrasting or diverse opinions discussed online. By better understanding the motivations and narratives of non-compliers, institutions may be better equipped to foster dialogue and reduce reliance on force.
Keywords: Twitter, COVID-19, Trieste, protests, sentiment analysis, Negative binomial regression, Topic modelling, clustering
Received: 23 Oct 2024; Accepted: 26 Sep 2025.
Copyright: © 2025 Arletti, Candiracci and Cottone. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Alberto Arletti, alberto.arletti@phd.unipd.it
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