Integrating Social Network Structure into Online Feature Selection
Authors: Antonela Tommasel
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | 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. |