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..
Integrating Social Network Structure into Online Feature Selection
Authors: Antonela Tommasel
IJCAI 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Preliminary evaluations conducted on two real-world shorttexts datasets achieved promising results (Figure 1) when compared to traditional and state-of-the-art (e.g. [Wang et al., 2013; 2014; Zubiaga et al., 2015]) baselines specifically defined for social media, in batch and online settings. The obtained results exposed the limitations of pure content-based techniques for classifying social media short-texts. Hence, they evidenced the need of considering social information, and its advantages for selecting the most relevant feature set. Figure 1: Precision Results Twitter dataset |
| Researcher Affiliation | Academia | Antonela Tommasel ISISTAN Research Institute, CONICET-UNCPBA |
| Pseudocode | No | The paper describes steps for its approach but does not include a formally structured pseudocode or algorithm block. |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | No | The paper mentions "two real-world shorttexts datasets" and a "Twitter dataset" but does not provide concrete access information (e.g., a link, DOI, or specific citation with author and year) for them, nor does it explicitly state they are publicly available with access details. |
| Dataset Splits | No | The paper mentions training models but does not specify explicit training, validation, or test dataset splits (e.g., percentages, sample counts, or references to predefined splits). |
| Hardware Specification | No | The paper does not specify any details about the hardware used for running the experiments (e.g., specific GPU/CPU models, memory, or cloud resources). |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., programming languages, libraries, or solvers with their versions) used in the experiments. |
| Experiment Setup | No | The paper describes the general approach but does not include specific experimental setup details such as hyperparameter values, training configurations, or model initialization settings. |