Topical Analysis of Interactions Between News and Social Media
Authors: Ting Hua, Yue Ning, Feng Chen, Chang-Tien Lu, Naren Ramakrishnan
AAAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | By applying our model to large sets of news and tweets, we demonstrate its significant improvement over baseline methods and explore its power in the discovery of interesting patterns for real world cases. Experiment In this section, we first describe our evaluation datasets, and then compare our proposed NTIT model with existing stateof-the-art algorithms. |
| Researcher Affiliation | Academia | Ting Hua, Yue Ning, Feng Chen, Chang-Tien Lu, Naren Ramakrishnan Department of Computer Science, Virginia Tech Falls Church, VA 22043 Department of Computer Science, University at Albany-SUNY Albany, NY 12222 |
| Pseudocode | Yes | The generative process is described in Algorithm 1. Algorithm 1: Generation Process of NTTT model |
| Open Source Code | No | The paper does not provide a link or explicit statement about the availability of its source code for the described methodology. |
| Open Datasets | No | To construct News dataset and Twitter dataset for evaluation, we crawled publicly accessible data using RSS API and Twitter API 1. The paper describes the construction of its own dataset from publicly accessible APIs but does not provide access information (link, DOI, citation) to the *constructed* dataset itself. |
| Dataset Splits | No | The paper does not provide specific percentages or sample counts for training, validation, or test splits. It refers to "evaluation datasets" generally. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., CPU, GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions using RSS API and Twitter API, and Gibbs sampling, but does not specify software names with version numbers (e.g., Python 3.x, PyTorch 1.x). |
| Experiment Setup | No | The paper describes its model, inference method, and data collection process, but it does not provide specific experimental setup details such as hyperparameter values (e.g., learning rate, batch size, number of epochs) or specific optimizer settings. |