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..
Retaining Data from Streams of Social Platforms with Minimal Regret
Authors: Nguyen Thanh Tam, Matthias Weidlich, Duong Chi Thang, Hongzhi Yin, Nguyen Quoc Viet Hung
IJCAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on large-scale real-world datasets illustrate the feasibility of our approach in terms of both, runtime and information quality. |
| Researcher Affiliation | Academia | 1 Ecole Polytechnique F ed erale de Lausanne, 2 Humboldt-Universit at zu Berlin, 3 The University of Queensland, 4 Grif๏ฌth University |
| Pseudocode | Yes | Algorithm 1: A Progressive Retaining Algorithm |
| Open Source Code | No | The paper does not provide any statement about releasing the source code for the methodology or a link to a code repository. |
| Open Datasets | No | The paper mentions extracting datasets using the Twitter Streaming API ("We extracted datasets using the Twitter Streaming API."), but it does not provide a direct link, DOI, specific repository name, or a formal citation to make these collected datasets publicly available. |
| Dataset Splits | No | The paper describes how portions of the data were used for evaluation (e.g., "select 100K items E from the original datasets and construct a set of retained items S as the k = 1% oldest items in E. We stream E and learn model parameters online"), but it does not specify explicit training, validation, and test dataset splits with percentages or sample counts. |
| Hardware Specification | Yes | All results have been obtained on an Intel i7 3.8GHz system (4 cores, 16GB RAM). |
| Software Dependencies | No | The paper mentions using "existing frameworks [Zhuang et al., 2016]" for social features but does not provide specific software names with version numbers (e.g., Python, PyTorch, TensorFlow, specific libraries or solvers with versions) used for their implementation. |
| Experiment Setup | Yes | Following [Hoffman et al., 2013], we vary the forget rate in (0.5, 1], choose a stable window size = 10 and report average values. |