# Social Media and Misleading Information in a Democracy: A Mechanism Design Approach

arXiv: https://arxiv.org/abs/2003.07192 · HTML: https://mlsanity.com/paper/2003.07192/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Mar 2020.

## Built on by (most cited first, 5 of 5)

- [A Framework for Effective AI Recommendations in Cyber-Physical-Human Systems](https://mlsanity.com/paper/2403.05715/) (2024)
- [Approximate Information States for Worst-Case Control and Learning in Uncertain Systems](https://mlsanity.com/paper/2301.05089/) (2023)
- [Worst-Case Control and Learning Using Partial Observations Over an Infinite Time-Horizon](https://mlsanity.com/paper/2303.16321/) (2023)
- [Off-Policy Evaluation for Sequential Persuasion Process with Unobserved Confounding](https://mlsanity.com/paper/2504.01211/) (2025)
- [Gradient Manipulation in Distributed Stochastic Gradient Descent with Strategic Agents: Truthful Incentives with Convergence Guarantees](https://mlsanity.com/paper/2603.27962/) (2026)

## Abstract (arXiv)

In this paper, we present a resource allocation mechanism for the problem of incentivizing filtering among a finite number of strategic social media platforms. We consider the presence of a strategic government and private knowledge of how misinformation affects the users of the social media platforms. Our proposed mechanism incentivizes social media platforms to filter misleading information efficiently, and thus indirectly prevents the spread of fake news. In particular, we design an economically inspired mechanism that strongly implements all generalized Nash equilibria for efficient filtering of misleading information in the induced game. We show that our mechanism is individually rational, budget balanced, while it has at least one equilibrium. Finally, we show that for quasi-concave utilities and constraints, our mechanism admits a generalized Nash equilibrium and implements a Pareto efficient solution.