# Rationally Inattentive Inverse Reinforcement Learning Explains YouTube Commenting Behavior

arXiv: https://arxiv.org/abs/1910.11703 · HTML: https://mlsanity.com/paper/1910.11703/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Placed in Inverse reinforcement learning, one of 13 directions in Offline RL and imitation learning from data.
- Builds on 10 of its 79 references that are in the map.
- First posted on arXiv 24 Oct 2019.

## Placed in

- Offline RL and imitation learning from data / [Inverse reinforcement learning](https://mlsanity.com/offlinerl/inverse-reinforcement-learning/)

## Builds on (most cited first, 10 of 10)

- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Deep Networks with Stochastic Depth](https://mlsanity.com/paper/1603.09382/) (2016)
- [Unsupervised Deep Embedding for Clustering Analysis](https://mlsanity.com/paper/1511.06335/) (2015)
- [Learning Robust Rewards with Adversarial Inverse Reinforcement Learning](https://mlsanity.com/paper/1710.11248/) (2017)
- [Inverse Reward Design](https://mlsanity.com/paper/1711.02827/) (2017)
- [A Primer on Neural Network Models for Natural Language Processing](https://mlsanity.com/paper/1510.00726/) (2015)
- [Resourceful Contextual Bandits](https://mlsanity.com/paper/1402.6779/) (2014)
- [MatConvNet - Convolutional Neural Networks for MATLAB](https://mlsanity.com/paper/1412.4564/) (2014)

## Built on by (most cited first, 6 of 6)

- [Behavioral Homophily in Social Media via Inverse Reinforcement Learning: A Reddit Case Study](https://mlsanity.com/paper/2502.02943/) (2025)
- [Learning the Optimal Recommendation from Explorative Users](https://mlsanity.com/paper/2110.03068/) (2021)
- [Necessary and Sufficient Conditions for Inverse Reinforcement Learning of Bayesian Stopping Time Problems](https://mlsanity.com/paper/2007.03481/) (2020)
- [Solving Dynamic Principal-Agent Problems with a Rationally Inattentive Principal](https://mlsanity.com/paper/2202.01691/) (2022)
- [Rationally Inattentive Utility Maximization for Interpretable Deep Image Classification](https://mlsanity.com/paper/2102.04594/) (2021)
- [Machine Learning enabled models for YouTube Ranking Mechanism and Views Prediction](https://mlsanity.com/paper/2211.11528/) (2022)

## Abstract (arXiv)

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'enyi 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.