Identifying Domain-Dependent Influential Microblog Users: A Post-Feature Based Approach
Authors: Nian Liu, Lin Li, Guandong Xu, Zhenglu Yang
AAAI 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experimental results show that the post-feature based approach produces relatively higher precision than that of the content based approach. |
| Researcher Affiliation | Academia | Nian Liu, Lin Li School Of Computer Science & Technology Wuhan University Of Technology Wuhan 430070, China {liunian, cathylilin}@whut.edu.cn Guandong Xu Advanced Analytics Institute University of Technology, Sydney NSW 2007, Australia Guandong.Xu@uts.edu.au Zhenglu Yang College Of Computer & Control Engineering Nankai University Tianjin 300071, China thxlifeyzl@gmail.com |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper provides a general research website link 'https://sites.google.com/site/gdxuau/' but does not explicitly state that the source code for the methodology is released or provide a direct link to a code repository. |
| Open Datasets | No | The paper states 'We extract dataset from sina microblog (weibo.com)' but does not provide concrete access information like a specific link, DOI, repository name, or formal citation for public availability of the extracted dataset. |
| Dataset Splits | No | The paper states 'we randomly devide our whole data set into training set (60%) and testing set(40%)' but does not explicitly provide a separate validation dataset split with specific percentages or counts. |
| Hardware Specification | No | The paper does not provide any specific hardware details (like CPU/GPU models or memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions using SVM but does not specify any software names with version numbers (e.g., library versions or specific software packages). |
| Experiment Setup | No | The paper does not provide specific experimental setup details such as hyperparameter values (e.g., learning rate, batch size, kernel type for SVM) or explicit training configurations. |