Measuring the co-evolution of online engagement with (mis)information and its visibility at scale

A team of researchers from institutions including the University of Oxford and the University of Trento has published a comprehensive study analyzing the relationship between online engagement with (mis)information and user visibility. Using a massive dataset of over 100 million COVID-19-related retweets spanning three years, the researchers tracked how user interactions and follower dynamics differed for factual, misleading, and uncertain content. Their key finding reveals a concerning pattern: while factual content creators experience rapid follower gain spikes during major events like vaccine rollouts, those spreading misleading content tend to sustain faster growth outside these high-attention periods.

The study introduces two novel modeling frameworks—simple contagion and biased convergence—that successfully reproduce the observed differing follower growth rates using temporal retweet network dynamics. These models provide strong evidence that content visibility co-evolves with user engagement in predictable ways. The researchers found that the dynamics of misinformation spread create a self-reinforcing cycle where engagement boosts visibility, which in turn drives further engagement. This work represents one of the largest-scale analyses of misinformation dynamics to date, offering new tools for understanding how attention economies function during crises.

The implications extend beyond the COVID-19 pandemic context, as the modeling frameworks can be applied to study other large-scale events where online attention is contested, such as climate change debates and political elections. The research provides quantitative evidence for what many have suspected: that the architecture of social media platforms may inadvertently reward the spread of misleading content through follower growth mechanisms, even when that content doesn't dominate during peak news cycles.