  We consider a novel application of inverse reinforcement learning with
behavioral economics constraints to model, learn and predict the commenting
behavior of YouTube viewers. Each group of users is modeled as a rationally
inattentive Bayesian agent which solves a contextual bandit problem. Our
methodology integrates three key components. First, to identify distinct
commenting patterns, we use deep embedded clustering to estimate framing
information (essential extrinsic features) that clusters users into distinct
groups.Second, we present an inverse reinforcement learning algorithm that uses
Bayesian revealed preferences to test for rationality: does there exist a
utility function that rationalizes the given data, and if yes, can it be used
to predict commenting behavior? Finally, we impose behavioral economics
constraints stemming from rational inattention to characterize the attention
span of groups of users. The test imposes a R{\'e}nyi mutual information cost
constraint which impacts how the agent can select attention strategies to
maximize their expected utility. After a careful analysis of a massive YouTube
dataset, our surprising result is that in most YouTube user groups, the
commenting behavior is consistent with optimizing a Bayesian utility with
rationally inattentive constraints. The paper also highlights how the rational
inattention model can accurately predict commenting behavior. The massive
YouTube dataset and analysis used in this paper are available on GitHub and
completely reproducible.
