Empirical Success in Prebunking

14 Jun 2024


(1) Yigit Ege Bayiz, Electrical and Computer Engineering The University of Texas at Austin Austin, Texas, USA (Email: egebayiz@utexas.edu);

(2) Ufuk Topcu, Aerospace Engineering and Engineering Mechanics The University of Texas at Austin Austin, Texas, USA (Email: utopcu@utexas.edu).

Abstract and Introduction

Related Works


Optimal Prebunking Problem

Deterministic Baseline Policies

Temporally Equidistant Prebunking

Numerical Results

Conclusion and References


We define the problem of optimally delivering prebunks to a user as a minimax optimization problem, and under SI propagation assumptions propose algorithms that guarantee feasibility. We demonstrate that our theoretically backed approach Algorithm 3 also yields better results than the other two baselines in empirical analysis using simulated misinformation propagations on Chung-Lu models. Algorithm 3 is also often computationally feasible to solve, as at each time, it relies on solving a linear program that is computationally inexpensive.

Our results, however, suffer from the limitations we impose on the network structure. Real-world misinformation propagation often deviates significantly from the SI model predictions. Our models also only provide feasibility guarantees under the discrete-time epidemic propagation assumptions. We also focus solely on delivering optimal prebunks to each user one by one, which is different from optimizing prebunk deliveries on the entire network. For future work, we plan to extend our problem and results to optimizing misinformation deliveries across the entire social network.


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