Detecting Promotion Campaigns in Community Question Answering
Authors: Xin Li, Yiqun Liu, Min Zhang, Shaoping Ma, Xuan Zhu, Jiashen Sun
IJCAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results based on more than 6 million entries from a popular Chinese CQA portal show that our approach outperforms a number of existing quality estimation methods for detecting promotion campaigns on both the answer level and QA pair level. |
| Researcher Affiliation | Collaboration | State Key Laboratory of Intelligent Technology and Systems Tsinghua National Laboratory for Information Science and Technology Department of Computer Science and Technology, Tsinghua University, Beijing, 100084, China Samsung R&D Institute China Beijing |
| Pseudocode | Yes | Algorithm 1 Answerer-channel bipartite graph propagation algorithm |
| Open Source Code | No | The paper does not provide any explicit statements about making the source code for their methodology publicly available, nor does it include links to a code repository. |
| Open Datasets | No | The paper states, 'With the help of a popular Chinese CQA portal named Sogou Wenwen (http://wenwen.sogou.com/), we collect 6,452,981 entries', but it does not provide concrete access information or explicitly state that the collected dataset is publicly available. |
| Dataset Splits | Yes | In each of the experiments, we apply a logistic regression model and use 10-fold cross validation. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run the experiments, such as CPU or GPU models, or cloud computing specifications. |
| Software Dependencies | No | The paper mentions using a 'logistic regression model' for classification but does not specify any software dependencies with version numbers, such as programming languages, libraries, or frameworks used for implementation. |
| Experiment Setup | No | The paper describes the overall framework and features used, but it does not provide specific experimental setup details such as hyperparameter values for the logistic regression model or any other specific training configurations. |