How to Detect Information Voids Using Longitudinal Data from Social Media and Web Searches
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866915801713344512 |
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| author | Scalco, Irene Gesualdo, Francesco Cerqueti, Roy Cinelli, Matteo |
| author_facet | Scalco, Irene Gesualdo, Francesco Cerqueti, Roy Cinelli, Matteo |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_15476 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | How to Detect Information Voids Using Longitudinal Data from Social Media and Web Searches Scalco, Irene Gesualdo, Francesco Cerqueti, Roy Cinelli, Matteo Computers and Society Physics and Society 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. |
| title | How to Detect Information Voids Using Longitudinal Data from Social Media and Web Searches |
| topic | Computers and Society Physics and Society |
| url | https://arxiv.org/abs/2602.15476 |