Social Media based Simulation Models for Understanding Disease Dynamics
Authors: Ting Hua, Chandan K Reddy, Lei Zhang, Lijing Wang, Liang Zhao, Chang-Tien Lu, Naren Ramakrishnan
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In various experiments, our proposed model outperforms current disease forecasting approaches with better accuracy and more stability. |
| Researcher Affiliation | Academia | 1 Virginia Tech, Department of Computer Science 2 George Mason University, Department of Information Science and Technology |
| Pseudocode | Yes | Algorithm 1: generation process of words in social media space of SMS model. |
| Open Source Code | No | The paper does not provide an explicit statement or link for the open-sourcing of the described methodology's code. |
| Open Datasets | No | The paper describes the collection and annotation of their own Twitter datasets (D1 and D2) but does not provide specific access information (link, DOI, or repository) for these datasets to be publicly available. |
| Dataset Splits | No | The paper mentions 'training set D1 and testing set D2' but does not specify exact percentages or sample counts for these splits, nor does it explicitly mention a validation set. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers for its implementation. |
| Experiment Setup | No | The paper describes some settings for comparison methods (e.g., orders for Lin ARX and Log ARX) but does not provide specific experimental setup details or hyperparameters for its own SMS model, such as learning rates, batch sizes, or number of epochs. |