Abstract

Citation

Researchers should cite this work as follows:

• Patterson, B., Pattersonq, S. (2022). 6-009-FakeNews-ModelingScenario. SIMIODE, QUBES Educational Resources. doi:10.25334/1BSK-FA06

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Article Context

Description

Clickbait, propaganda, misleading headlines, biased news outlets, and social media posts all have the potential to fuel the wildfire of fake news. In this project, you will model the spread of fake news and investigate ways to deter distributing misinformation.

One way to model the spread of disease is the SIR model. This models the flow of people between three states: susceptible (S), infected (I), and resistant (R), where S, I, and R each represent the number of people in each of those states. However, we can also model the spread of information using this same model. In this context susceptible would represent those who have not been exposed to fake news but are willing to believe it, infected would represent those who currently believe fake news, and resistant would represent those who are not willing to believe fake news. This latter case could be because they learned that the information was a hoax.