Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Rumor Detection on Social Media with Graph Structured Adversarial Learning
Authors: Xiaoyu Yang, Yuefei Lyu, Tian Tian, Yifei Liu, Yudong Liu, Xi Zhang
IJCAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on real-world datasets demonstrate that our model achieves better results than the state-of-the-art methods. |
| Researcher Affiliation | Academia | Key Laboratory of Trustworthy Distributed Computing and Service (BUPT), Ministry of Education, Beijing University of Posts and Telecommunications, Beijing, China |
| Pseudocode | Yes | Algorithm 1 Model training with a minimax game |
| Open Source Code | No | The paper does not provide an explicit statement or link for the open-source code of the methodology described. |
| Open Datasets | Yes | We evaluate our model on two real-word datasets: Weibo [Song et al., 2018] and Twitter [Zubiaga et al., 2016] |
| Dataset Splits | Yes | We split the datasets for training, developing, and testing with a ratio of 7:1:2. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory amounts, or specific cloud instances) used for running its experiments. |
| Software Dependencies | No | The paper mentions 'Adam algorithm [Kingma and Ba, 2015]' but does not list any specific software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | The learning rate used in the training process is 0.002. |