Understanding the Spread of Misinformation: Shedding Light on the Beliefs of Rational Individuals

Misinformation is unfortunately prevalent on social media platforms like Facebook, Twitter, and Instagram. False information about topics ranging from vaccines to the war in Ukraine to climate change can be found easily.

While some individuals are able to differentiate between truth and fiction, others struggle to do so.

“PolyGraphs: Combatting Networks of Ignorance in the Misinformation Age” aims to answer the question of how rational individuals can come to believe false information. This research project, conducted by Northeastern University London’s departments of philosophy, economics, and computer science, utilizes computer simulations to gain insights into the flow of knowledge within social media communities.

After two years of research, the team has launched an interactive website and made significant discoveries, including insights into the process and reasons behind rational people embracing incorrect information.

The project combines artificial data with real social network data from platforms like Facebook and Twitter to create simulated communities. In these simulations, each individual is tasked with choosing between options A and B, with B being the correct choice. The individuals collect their own evidence, share it with others, and adjust their beliefs accordingly. The researchers then analyze whether the community collectively reaches the correct conclusion and how long it takes.

Simulations akin to drug trials

To provide context to the simulations, Amil Mohanan, an assistant professor in philosophy at Northeastern University London, compares them to drug trials conducted by a community of doctors. In the simulation, each doctor is assigned either drug A or drug B for testing.

“We know that B is slightly better, but the doctors in the simulated community don’t,” Mohanan explains.

As the doctors conduct trials, they discover that drug B is superior and share their findings with their peers. Depending on various factors, some doctors revise their beliefs based on what they learn from others. Ideally, they eventually reach a consensus that drug B is the better option.

The researchers measure how long it takes for communities to figure out that drug B is superior by conducting thousands of iterations of the simulations and manipulating parameters such as network size and structure.

For example, one type of network only allows information flow between each agent and two others, forming a sharing circle. Another type involves one person sharing information with the entire group, which reciprocates the information. Yet another type facilitates information sharing between all members. These network types mirror real-life or online networks.

The impact of small lies

Misinformation also plays a role in the simulation results. Mohanan has discovered that small lies can have a significant impact, depending on the density of the network.

In different simulations, lying takes various forms. Doctors can pretend to conduct a trial and claim that they know drug A is better. Alternatively, they can randomly select one option and fabricate data. They can also choose drug A simply because they are more familiar with it.

Each scenario yields different outcomes, and after thousands of simulation iterations, the research team has found that even small seeds of doubt can have a substantial influence.

The team has also encountered unexpected findings. Contrary to expectations, more information sharing among individuals can sometimes impede consensus formation—a phenomenon known as the Zollman Effect. This theory suggests that increased connectivity can lead to mistaken beliefs.

“Rational agents in a network like this can end up ignorant more often or more likely to fail to arrive at the true answer to the question if they talk to one another more,” explains Brian Ball, head of faculty in philosophy at Northeastern University London.

The researchers also observed that when community members lack trust in those with differing beliefs, consensus becomes elusive, resulting in polarization.

Ignorance among rational individuals

Above all, the researchers aim to highlight the prevalence of ignorance even among rational individuals. People may assume that those who embrace misinformation are unintelligent or prejudiced. However, Ball states, “We show that actually, people can get it wrong when it has nothing to do with that.”

Instead, individuals can be misled due to their network structure and information environment.

Ball hopes that these findings can be useful in various contexts, such as social media, public policy, media organizations, and non-profit organizations focused on combating online misinformation.

If you’re concerned about what you encounter on social media, Mohanan offers reassurance—truth generally emerges over time.

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