How to Detect Information Voids Using Longitudinal Data from Social Media and Web Searches

2026-02-17

AI Summary

This study investigates the phenomenon of “information vacuums”—defined as the absence of reliable information on specific topics—and its relationship with the spread of misinformation. By integrating longitudinal, multi-source data from Facebook, Twitter, Google, Wikipedia, and news websites, the authors employ cross-platform temporal analysis and information quality assessment to conceptualize information vacuums as a structural state in information dissemination, while simultaneously quantifying their counterpart, “information overload.” The findings demonstrate that information vacuums significantly predict heightened misinformation activity, thereby confirming their pivotal role in infodemics. This work offers a novel theoretical perspective and empirical foundation for understanding the mechanisms underlying the emergence of misleading content and for designing effective intervention strategies.

Technology Category

• Application Domains: Misinformation & Fake News
• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community

Application Category

• Social Networks and Social Media: Influence propagation, information diffusion, and the prediction on networks
• Web Mining and Content Analysis: Content-based information diffusion
• Responsible Web: Mitigating misinformation and disinformation

Abstract

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.

Problem

• information voids
• misinformation
• attention economy
• infodemic
• longitudinal data

Innovation

• information voids
• attention economy
• longitudinal data
• misinformation
• infodemic