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

TL;DR

This paper introduces a method to detect and analyze information voids on social media and web platforms, demonstrating their role in misinformation spread during the COVID-19 vaccine rollout across six European countries.

Contribution

It develops a novel approach to identify and quantify information voids using longitudinal data, linking them to misinformation and expanding understanding of infodemic dynamics.

Findings

Information voids correlate with increased misinformation.

Information voids emerge during critical information gaps.

Detection method applied to COVID-19 vaccine data.

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.