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..
Twitter Summarization Based on Social Network and Sparse Reconstruction
Authors: Ruifang He, Xingyi Duan
AAAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on this datasets show the effectiveness of our framework for handling the large scale short and noisy messages in social media. |
| Researcher Affiliation | Academia | Ruifang He, Xingyi Duan School of Computer Science and Technology, Tianjin University, Tianjin 300350, China Tianjin Key Laboratory of Cognitive Computing and Application, Tianjin 300350, China |
| Pseudocode | Yes | Algorithm 1 An Efficient Optimization Algorithm for SNSR |
| Open Source Code | No | The paper does not provide a statement or link indicating that the source code for their methodology is publicly available. |
| Open Datasets | Yes | We use the public Twitter data collected by University of Illinois1 as the raw data. 1https://wiki.illinois.edu/wiki/display/forward/Dataset-UDI -Twitter Crawl-Aug2012 |
| Dataset Splits | No | The paper describes the creation of a "ground truth" corpus for evaluation, but does not specify explicit training, validation, and test splits for model development or hyperparameter tuning. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run the experiments. |
| Software Dependencies | No | The paper does not list specific software components with version numbers required to replicate the experiments. |
| Experiment Setup | Yes | We tune four parameters greedily through setting step size, such as α in range [0, 1], setting step size as 0.01. Through preliminary experiments, we set α = 0.03, λ = 1, and γ = 1. And we set the similarity threshold θ = 0.1, which is consistent with the observation that the similarity between sentences is mostly distributed in the area of [0, 0.1]. |