arXiv subjects

Anna Bertani

Publications and source records attributed to Anna Bertani.

4 recordsLinked to original sources

arXiv2026

Socioeconomic Determinants of the COVID-19 Infodemic

The COVID-19 pandemic has been accompanied by an infodemic of misinformation that impedes effective public health responses. This study examines relationships between socioeconomic factors and infodemic risk patterns across 37 OECD countries using Twitter data from 2020-2022. Employing dimensionality reduction techniques on 20 socioeconomic indicators, we identify complex correlations with infodemic measures that evolve throughout the pandemic. Countries exhibit distinct clustering in their infodemic profiles that transcend conventional socioeconomic categorizations. We find that dynamic information behaviors dominate initial crisis responses, while stable socioeconomic conditions become more influential as the pandemic progresses. News media diet diversity emerges as a significant protective factor, with pluralistic information ecosystems demonstrating greater resilience against misinformation. Additionally, institutional stability correlates strongly with reduced infodemic volatility over time. These findings highlight how infodemics are embedded within broader socioeconomic contexts, providing foundations for targeted interventions to build societal resilience against misinformation during future health emergencies.

physics.soc-ph↗

arXiv2025

From Birdwatch to Community Notes, from Twitter to X: four years of community-based content moderation

Community Notes (formerly known as Birdwatch) is the first large-scale crowdsourced content moderation initiative launched by X (formerly Twitter) in January 2021. As the Community Notes model gains momentum across other social media platforms, there is a growing need to assess its underlying dynamics and effectiveness. This paper provides a descriptive investigation of Community Notes during its first four years, examining its linguistic diversity, sourcing practices, Contributor activity, rating behaviour, and interaction networks. In addition, we release a curated dataset and accompanying source code to support future research, along with a review of prior research on Community Notes. We parsed Notes and ratings data from the first four years of the program and conducted language detection across all Notes. For English-language Notes, we extracted embedded URLs and identified discussion topics in each Note. Additionally, we constructed monthly interaction networks among the Contributors. Together, the descriptive analysis, dataset, code, and literature review provide a foundation for advancing research on Community Notes and community-based content moderation more broadly.

cs.SI↗

arXiv2025

Mapping the interaction between science and misinformation in COVID-19 tweets

During the COVID-19 pandemic, scientific knowledge evolved rapidly, accompanied by a surge of misinformation, labelled an infodemic by the WHO. In this context, we study the interaction between science and misinformation on Twitter (now X) using a database of ~407M COVID-19-related tweets. We classify URL reliability with Media Bias/Fact Check and used Altmetric data to identify scientific publications. We find that among ~1.2M users who shared science, 45% also shared unreliable content. Scientific papers circulated by these users were more often preprints, slightly more likely to be retracted, less cited, and published in lower-impact journals. Our findings indicate misinformation is not driven by a lack of exposure to science but instead raise critical questions about open science practices, particularly the role of preprints in amplifying misleading narratives. Our results underscore the importance of proactive scientific engagement on social media in countering misinformation and reinforcing trust in science during global crises.

physics.soc-ph↗

arXiv2024

Decoding the News Media Diet of Disinformation Spreaders

In the digital era, information consumption is predominantly channeled through online news media disseminated on social media platforms. Understanding the complex dynamics of the news media environment and users habits within the digital ecosystem is a challenging task that requires at the same time large bases of data and accurate methodological approaches. This study contributes to this expanding research landscape by employing network science methodologies and entropic measures to analyze the behavioural patterns of social media users sharing news pieces and dig into the diverse news consumption habits within different online social media user groups. Our analyses reveal that users are more inclined to share news classified as fake when they have previously posted conspiracy or junk science content, and vice versa, creating a series of misinformation hot streaks. To better understand these dynamics, we used three different measures of entropy to gain insights into the news media habits of each user, finding that the patterns of news consumption significantly differ among users when focusing on disinformation spreaders, as opposed to accounts sharing reliable or low-risk content. Thanks to these entropic measures, we quantify the variety and the regularity of the news media diet, finding that those disseminating unreliable content exhibit a more varied and at the same time a more regular choice of web domains. This quantitative insight into the nuances of news consumption behaviours exhibited by disinformation spreaders holds the potential to significantly inform the strategic formulation of more robust and adaptive social media moderation policies.

physics.soc-ph↗