Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Online Reputation Fraud Campaign Detection in User Ratings
Authors: Chang Xu, Jie Zhang, Zhu Sun
IJCAI 2017 | Venue PDF | LLM Run Details | Input Tokens: 15,492 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,901 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical analysis on two real-world datasets validates the effectiveness and efficiency of the proposed framework. |
| Researcher Affiliation | Academia | Chang Xu, Jie Zhang, Zhu Sun School of Computer Science and Engineering, Nanyang Technological University, Singapore EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1: Incremental RFC Detection Flow |
| Open Source Code | No | The paper does not include any explicit statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | Our experiments are conducted on two real-world datasets. Restaurant Reviews on Yelp (Yelp Zip): This dataset was used in [Rayana and Akoglu, 2015]... Product Reviews on Amazon (Amazon Cn): This dataset was created by [Xu et al., 2013]. |
| Dataset Splits | No | The paper mentions 'training data' but does not provide specific percentages, sample counts, or a detailed methodology for dataset splits (e.g., for train, validation, or test sets). |
| Hardware Specification | Yes | The experiments are conducted on a machine with a single-CPU, 3.20Ghz and 16G memory. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., library names, frameworks, or solvers with their versions) used to replicate the experiment. |
| Experiment Setup | Yes | For FRAUDSCAN and its variants, four parameters α1, α2, α3, and k need to be tuned... Here, the optimal settings (α1, α2, α3, k) are used, i.e., (0.1, 0.1, 0.01, 20) for Amazon Cn and (0.01, 0.1, 0.01, 30) for Yelp Zip. |