A Persuasive Approach to Combating Misinformation
Authors: Safwan Hossain, Andjela Mladenovic, Yiling Chen, Gauthier Gidel
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Lastly, we experimentally validate that our approach significantly reduces misinformation in both the single round and performative setting. and 7. Experiments We now experimentally validate our approach: specifically, while we provide detailed theoretical results on the platform utility under optimal signaling, it is instructive to see how this translates into reducing misinformation sharing. |
| Researcher Affiliation | Academia | 1Harvard University 2Mila, Universit e de Montr eal. |
| Pseudocode | No | No structured pseudocode or algorithm blocks are provided. The paper describes mathematical formulations and theoretical proofs. |
| Open Source Code | No | The paper does not contain any explicit statement about releasing source code or a link to a code repository for the described methodology. |
| Open Datasets | No | Due to a lack of public data, we create a synthetic dataset for the three components of a noisy persuasion instance: the prior distribution, platform utility, and user utility. There is no access information provided for this synthetic dataset. |
| Dataset Splits | No | The paper does not provide specific dataset split information (percentages, sample counts, or citations to predefined splits) for training, validation, and test sets. It mentions 'validation/popularity states' and 'validation classifier' in the context of its model, not data splits. |
| Hardware Specification | No | The paper does not provide any specific hardware details (e.g., GPU/CPU models, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | No | The paper describes how the synthetic dataset was created and how classifier error was varied ('For each instance, we vary the classification error between 0 to 0.4 (the error is equally divided amongst all the incorrect classes)'), but it does not provide specific hyperparameters or training configurations for any model that would be used in an experiment. |