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..
Modelling Individual Negative Emotion Spreading Process with Mobile Phones
Authors: Zhanwei Du, Yongjian Yang, Chuang Ma, Yuan Bai
AAAI 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Taking the MIT Social Evolution dataset as an example, the experimental results verify the efficacy of our techniques on real-world data. |
| Researcher Affiliation | Academia | Zhanwei Du,Yongjian Yang, Chuang Ma, Yuan Bai Jilin University Changchun, 130012 PRC |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statements about releasing open-source code for the described methodology. |
| Open Datasets | Yes | GCHMSM is tested on the MIT Social Evolution Dataset (Madan et al. 2012). |
| Dataset Splits | No | The paper mentions using the MIT Social Evolution Dataset for a specific period but does not provide explicit training, validation, or test dataset splits or percentages. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., CPU, GPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper mentions using a 'Gibbs Sampling method' but does not provide specific software names or version numbers for any libraries or dependencies. |
| Experiment Setup | No | The paper describes the model parameters and inference method but does not provide specific experimental setup details such as hyperparameter values or training configurations. |