Defending Elections against Malicious Spread of Misinformation
Authors: Bryan Wilder, Yevgeniy Vorobeychik2213-2220
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results confirm that our algorithms provide nearoptimal defender strategies and showcase variations in the difficulty of defending elections depending on the resources and knowledge available to the defender. Our experiments use the Yahoo webscope dataset (Yahoo 2007). |
| Researcher Affiliation | Academia | 1Center for Artificial Intelligence in Society, University of Southern California, bwilder@usc.edu 2Department of Computer Science & Engineering, Washington University in St. Louis, yvorobeychik@wustl.edu |
| Pseudocode | Yes | Algorithm 1 FPLT(ϵ) and Algorithm 2 Online Gradient(η, α, T, ka) are provided. |
| Open Source Code | No | No concrete statement or link regarding the availability of the methodology's source code is provided. |
| Open Datasets | Yes | Our experiments use the Yahoo webscope dataset (Yahoo 2007). |
| Dataset Splits | No | The paper mentions 'train', 'validation', and 'test' in the context of the problem but does not specify the actual data splits used for its experiments with exact percentages or sample counts. |
| Hardware Specification | No | No specific hardware details (e.g., CPU, GPU models, memory, or cloud instance types) are provided for the experimental setup. |
| Software Dependencies | No | No specific software dependencies with version numbers (e.g., Python version, library versions) are mentioned. |
| Experiment Setup | Yes | We use T = 50 iterations with η = 0.05. Each propagation probability is drawn uniformly at random from [0, 0.2] for each player. Each voter s preference is also drawn uniformly at random. |