  The spread of disinformation on social platforms is harmful to society. This
harm may manifest as a gradual degradation of public discourse; but it can also
take the form of sudden dramatic events such as the 2021 insurrection on
Capitol Hill. The platforms themselves are in the best position to prevent the
spread of disinformation, as they have the best access to relevant data and the
expertise to use it. However, mitigating disinformation is costly, not only for
implementing detection algorithms or employing manual effort, but also because
limiting such highly viral content impacts user engagement and potential
advertising revenue. Since the costs of harmful content are borne by other
entities, the platform will therefore have no incentive to exercise the
socially-optimal level of effort. This problem is similar to that of
environmental regulation, in which the costs of adverse events are not directly
borne by a firm, the mitigation effort of a firm is not observable, and the
causal link between a harmful consequence and a specific failure is difficult
to prove. For environmental regulation, one solution is to perform costly
monitoring to ensure that the firm takes adequate precautions according to a
specified rule. However, a fixed rule for classifying disinformation becomes
less effective over time, as bad actors can learn to sequentially and
strategically bypass it. Encoding our domain as a Markov decision process, we
demonstrate that no penalty based on a static rule, no matter how large, can
incentivize optimal effort. Penalties based on an adaptive rule can incentivize
optimal effort, but counter-intuitively, only if the regulator sufficiently
overreacts to harmful events by requiring a greater-than-optimal level of
effort. We offer novel insights for the effective regulation of social
platforms, highlight inherent challenges, and discuss promising avenues for
future work.
