Large-scale analysis of COVID retweets finds factual content linked to rapid follower gains during major events while misleading content shows steadier growth otherwise, with two network models reproducing the patterns.
A general Markov chain approach for disease and rumour spreading in complex networks
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Measuring the co-evolution of online engagement with (mis)information and its visibility at scale
Large-scale analysis of COVID retweets finds factual content linked to rapid follower gains during major events while misleading content shows steadier growth otherwise, with two network models reproducing the patterns.