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 | Conference PDF | Archive PDF | Plain Text | 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. |