Combating Misinformation on Online Social Platform

Link to Paper (This work is published in 2023 21st International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt)): https://ieeexplore.ieee.org/abstract/document/10349848

Motivation

Misinformation has become a growing issue on online social platforms (OSPs), especially during elections or pandemics. To combat this, OSPs have implemented various policies, such as tagging, to notify users about potentially misleading information. However, these policies are often transparent and therefore susceptible to being exploited by content creators, who may not be willing to invest effort into producing authentic content, causing the viral spread of misinformation.

Instead of mitigating the reach of existing misinformation, we aim to focus on a solution of prevention, aiming to stop the spread of misinformation before it has a chance to gain mo-mentum.

Our approach

We propose a Bayesian persuaded branching process (BP^2) to model the strategic interactions among the online social platforrm (OSP), the content creator, and the platform user. The misinformation spread on OSP is modeled by a multi-type branching process, where users’ positive and negative comments influence the misinformation spreading. Using a Lagrangian induced by Bayesian plausibility, we characterize the OSP’s optimal policy under the perfect Bayesian equilibrium.

The Bayesian Persuaded Branching Process is shown below. The OSP first commits to an information structure $\pi$, followed by a private effort $\lambda$ exerted by the content creator, influencing the distribution of true/fake posts $\omega$. The users offer positive/negative comments to the post after observing the realized tag/label $s$, and then forward it to others.

Main Results

• The convexity of the Lagrangian implies that the OSP’s optimal policy is simply the fully informative tagging policy: revealing the content’s accuracy to the user.
• Such a truthful tagging policy solicits the best effort from the content creator in reducing misinformation, even though the OSP exerts no direct control over the content creator.

Some related research papers

Branching process

• S. Kapsikar, I. Saha, K. Agarwal, V. Kavitha, and Q. Zhu, “Controlling fake news by collective tagging: A branching process analysis,” IEEE Control Systems Letters, vol. 5, no. 6, pp. 2108–2113, 2021.

Bayesian persuasion

• E. Kamenica and M. Gentzkow, “Bayesian Persuasion,” American Economic Review, vol. 101, no. 6, pp. 2590–2615, 2011.