MISINFORMATIONMisinformation Really Does Spread Like a Virus, Suggest Mathematical Models Drawn from Epidemiology
When it comes to misinformation, “going viral” appears to be more than a simple catchphrase. Scientists have found a close analogy between the spread of misinformation and the spread of viruses: how misinformation gets around can be effectively described using mathematical models designed to simulate the spread of pathogens.
We’re increasingly aware of how misinformation can influence elections. About 73% of Americans report seeing misleading election news, and about half struggle to discern what is true or false.
When it comes to misinformation, “going viral” appears to be more than a simple catchphrase. Scientists have found a close analogy between the spread of misinformation and the spread of viruses. In fact, how misinformation gets around can be effectively described using mathematical models designed to simulate the spread of pathogens.
Concerns about misinformation are widely held, with a recent UN survey suggesting that 85% of people worldwide are worried about it.
These concerns are well founded. Foreign disinformation has grown in sophistication and scope since the 2016 US election. The 2024 election cycle has seen dangerous conspiracy theories about “weather manipulation” undermining proper management of hurricanes, fake news about immigrants eating pets inciting violence against the Haitian community, and misleading election conspiracy theories amplified by the world’s richest man, Elon Musk.
Recent studies have employed mathematical models drawn from epidemiology (the study of how diseases occur in the population and why). These models were originally developed to study the spread of viruses, but can be effectively used to study the diffusion of misinformation across social networks.
One class of epidemiological models that works for misinformation is known as the susceptible-infectious-recovered (SIR) model. These simulate the dynamics between susceptible (S), infected (I), and recovered or resistant individuals (R).
These models are generated from a series of differential equations (which help mathematicians understand rates of change) and readily apply to the spread of misinformation. For instance, on social media, false information is propagated from individual to individual, some of whom become infected, some of whom remain immune. Others serve as asymptomatic vectors (carriers of disease), spreading misinformation without knowing or being adversely affected by it.
These models are incredibly useful because they allow us to predict and simulate population dynamics and to come up with measures such as the basic reproduction (R0) number – the average number of cases generated by an “infected” individual.
As a result, there has been growing interest in applying such epidemiological approaches to our information ecosystem. Most social media platforms have an estimated R0 greater than 1, indicating that the platforms have potential for the epidemic-like spread of misinformation.
