This is our interview with Dorje Brody about his recent paper with David Meier, How to model fake news.
This paper uses the tools of communication theory and a sub-topic called filtering theory to describe the mathematical basis for an information channel which can contain fake news.
Our discussion covers the mechanics of the model developed in this research. Traditional communication theory approaches will often model a communication channel as a convolution of signal and noise. Often, that noise is assumed to be Guassian distributed. However, such an assumption does not seem right in the case of fake news, since it almost certainly has a bias. If, in a two party election, equal amounts of fake news were generated attacking each candidate, then perhaps such a model would apply. To capture the more real-world scenario, the authors develop a novel mechanism to model the presence and impact of fake news.
Our conversation covers the mechanics of the model, some discussion on model applicability, review of the simulation results, and several other fascinating points in this research.
The original paper, How to model fake news is available on the arXiv.