EKNOT: Event Knowledge from News and Opinions in Twitter
Authors: Min Li, Jingjing Wang, Wenzhu Tong, Hongkun Yu, Xiuli Ma, Yucheng Chen, Haoyan Cai, Jiawei Han
AAAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We present the EKNOT system that automatically discovers major events from online news articles, connects each event to its discussion in Twitter, and provides a comprehensive summary of the events from both news media and social media s point of view. EKNOT is built on real-time crawled news articles and tweets, allowing users to explore the dynamics of major events with minimal delays. |
| Researcher Affiliation | Academia | 1Department of Computer Science, University of Illinois at Urbana-Champaign, IL, USA 2School of Electronics Engineering and Computer Science, Peking University, Beijing, China 1{minli3, jwang112, wtong8, hyu50, ychen233, hcai6, hanj}@illinois.edu, 2xlma@pku.edu.cn |
| Pseudocode | No | No pseudocode or clearly labeled algorithm block was found in the paper. |
| Open Source Code | No | No explicit statement or link providing access to the open-source code for the described methodology was found. |
| Open Datasets | No | The paper mentions using 'real-time crawled news articles and tweets' and crawling data from 'Google news' and 'twitter API'. However, it does not provide concrete access information (link, DOI, citation with authors/year) for a publicly available or open dataset that could be used for reproduction. |
| Dataset Splits | No | The paper does not provide specific training/validation/test dataset split information (percentages, sample counts, or references to predefined splits) needed for reproduction. |
| Hardware Specification | No | No specific hardware specifications (e.g., GPU/CPU models, memory details) used for running experiments were mentioned in the paper. |
| Software Dependencies | No | The paper mentions using 'twitter API', 'BM25', and a 'two-step classification model (Barbosa and Feng 2010)', but it does not provide specific version numbers for any software libraries or dependencies. |
| Experiment Setup | No | The paper describes the system's modules and internal models but does not provide specific experimental setup details such as hyperparameter values, learning rates, batch sizes, or detailed training configurations. |