How to Detect Information Voids Using Longitudinal Data from Social Media and Web Searches
A new method analyzes social media and search data to find dangerous gaps in reliable information.
Researchers Irene Scalco, Francesco Gesualdo, Roy Cerqueti, and Matteo Cinelli developed a model that detects 'information voids'—periods where little reliable information exists on a topic. Using longitudinal data from Facebook, Google, Twitter, Wikipedia, and news outlets during the COVID-19 vaccine rollout in six European countries, they found these voids correlate with a higher prevalence of misinformation. The method quantifies both information voids and overabundance, providing a mechanistic explanation for how misinformation emerges online.
Why It Matters
This gives platforms and researchers a tool to identify and proactively fill dangerous information gaps before they are exploited.