  Understanding how individuals change their opinions is essential to mitigate
the harmful effect of fake news and stop opinion polarization. Building upon an
extremely flexible opinion dynamics model that can capture the key microscopic
mechanisms of social influence, the current paper elaborates a framework to
find optimal control, which, thus, can be applied to a broad set of opinion
dynamics settings. Using a combination of theoretical and computational
approaches, I characterize the properties of optimal control and demonstrates
how the elaborated framework can be applied to specific examples, which cover
the classical situations that garner remarkable attention in the literature on
opinion dynamics models: assimilative influence, bounded confidence, and
dissimilative influence.
