How to detect information voids using longitudinal data from social media and web searches

The model of the attention economy, where content producers compete for the attention of users, relies on two key forces: information supply and demand. This study leverages the feedback loop between these forces to develop a method for detecting and quantifying information voids, i.e., periods in which little or no reliable information is available on a given topic. Using a case study on COVID-19 vaccines rollout in six European countries, and drawing on data from multiple platforms including Facebook, Google, Twitter, Wikipedia, and online news outlets, we examine how information voids emerge, persist and correlate with a decline in the proportion of high-quality information circulating online.

By conceptualising information voids as a specific regime of information spreading, we also quantify their counterpart, information overabundance, which constitute a central component of the current definition of infodemic. We show that information voids are associated with a higher prevalence of misinformation, thus representing problematic hotspots in which individuals are more likely to be misled by low-quality online content. Overall, our findings provide empirical support for the inclusion of information voids in mechanistic explanations of misinformation emergence.

The authors received no financial support for the research. Department of Social Sciences and Economics, Sapienza University of Rome, P.le Aldo Moro, 5, Rome, 00185, Italy Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Via Savi, 10, Pisa, 56126, Italy Department of Computer Science, Sapienza University of Rome, Viale Regina Elena 295, Rome, 00161, Italy The authors declare no competing interests. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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How to detect information voids using longitudinal data from social media and web searches. Sci Rep (2026). https://doi.org/10.1038/s41598-026-69786-8 DOI: https://doi.org/10.1038/s41598-026-69786-8