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..
Modeling Scientific Influence for Research Trending Topic Prediction
Authors: Chengyao Chen, Zhitao Wang, Wenjie Li, Xu Sun
AAAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experiments conducted on a scientific dataset including conferences in artificial intelligence and data mining show that our model consistently outperforms the other state-of-the-art methods. |
| Researcher Affiliation | Academia | 1Department of Computing, The Hong Kong Polytechnic University, Hong Kong 2MOE Key Laboratory of Computational Linguistics, Peking University, China 3School of Electronics Engineering and Computer Science, Peking University, China |
| Pseudocode | No | The paper describes the model using mathematical equations but does not provide structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not include any explicit statements or links indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | We obtain the paper information of the above-mentioned conferences from a DBLP dataset published by (Tang et al. 2008) and updated in 2016. |
| Dataset Splits | Yes | The first 70% data is used for training, the following 15% data for the validation and the remaining 15% for testing. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for running the experiments, such as GPU/CPU models or other system specifications. |
| Software Dependencies | No | The paper mentions the use of GRU, continuous bag-of-words architecture, and the Adam algorithm, but it does not specify any software dependencies with version numbers. |
| Experiment Setup | Yes | The word representations with the dimensionality of 50 are trained... We also set the dimension of hidden state as 50 for CONI, CONI I and CONI V. |