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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
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. |