Title: A Bayesian Model for False Information Belief Impact, Optimal Design, and Fake News Containment,
Abstract: This work is a technical approach to modeling false information nature,
design, belief impact and containment in multi-agent networks. We present a
Bayesian mathematical model for source information and viewer's belief, and how
the former impacts the latter in a media (network) of broadcasters and viewers.
Given the proposed model, we study how a particular information (true or false)
can be optimally designed into a report, so that on average it conveys the most
amount of the original intended information to the viewers of the network.
Consequently, the model allows us to study susceptibility of a particular group
of viewers to false information, as a function of statistical metrics of the
their prior beliefs (e.g. bias, hesitation, open-mindedness, credibility
assessment etc.). In addition, based on the same model we can study false
information "containment" strategies imposed by network administrators.
Specifically, we study a credibility assessment strategy, where every
disseminated report must be within a certain distance of the truth. We study
the trade-off between false and true information-belief convergence using this
scheme which leads to ways for optimally deciding how truth sensitive an
information dissemination network should operate.