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 [1].
Information Credibility Evaluation on Social Media
Authors: Shu Wu, Qiang Liu, Yong Liu, Liang Wang, Tieniu Tan
AAAI 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Based on our algorithm, NICE system achieves satisfactory performance on evaluating information credibility and detecting rumors on social media. |
| Researcher Affiliation | Academia | Center for Research on Intelligent Perception and Computing (CRIPAC) National Laboratory of Pattern Recognition (NLPR) Institute of Automation, Chinese Academy of Sciences, China |
| Pseudocode | No | No structured pseudocode or algorithm blocks (e.g., labeled 'Algorithm' or 'Pseudocode') were found. |
| Open Source Code | No | No explicit statement or link indicating that the source code for the methodology is publicly available was found. |
| Open Datasets | No | The paper describes creating its own dataset from Sina Weibo, stating 'Finally, this database contains the veri๏ฌed rumors and non-rumors, which are used to train our model.' However, no concrete access information (link, DOI, repository name, or formal citation for public availability) for this dataset is provided. |
| Dataset Splits | No | No specific dataset split information (exact percentages, sample counts for train/validation/test, or cross-validation setup) is provided. The paper only states that a database was used to 'train our model'. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory, or cloud instance types) used for running experiments were mentioned. |
| Software Dependencies | No | No specific ancillary software details with version numbers (e.g., library or solver names like Python 3.8, CPLEX 12.4) were provided. |
| Experiment Setup | No | No specific experimental setup details, such as concrete hyperparameter values, training configurations, or system-level settings, were found in the main text. |