A Convolutional Approach for Misinformation Identification
Authors: Feng Yu, Qiang Liu, Shu Wu, Liang Wang, Tieniu Tan
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiment results on two large-scale datasets validate the effectiveness of CAMI model on both misinformation identification and early detection tasks. |
| Researcher Affiliation | Academia | 1Center for Research on Intelligent Perception and Computing National Laboratory of Pattern Recognition 2Center for Excellence in Brain Science and Intelligence Technology Institute of Automation, Chinese Academy of Sciences 3University of Chinese Academy of Sciences |
| Pseudocode | No | The paper describes the architecture and components of the CAMI model but does not include any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement about releasing source code for the methodology, nor does it include a link to a code repository. |
| Open Datasets | Yes | We evaluate models on two large microblog datasets: Weibo and Twitter dataset, which is developed and used by [Castillo et al., 2011; Kwon et al., 2013; Ma et al., 2016]. |
| Dataset Splits | Yes | In all experiments, we randomly choose 10% of dataset for model tuning and the rest 90% are randomly assigned in a 3:1 ratio for training and test. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run the experiments (e.g., GPU models, CPU types, memory specifications). |
| Software Dependencies | No | The paper mentions: "implemented with Theano4. 4http://deeplearning.net/software/theano/". While it names a software dependency (Theano), it does not specify a version number, which is required for reproducibility. |
| Experiment Setup | Yes | The parameters of CAMI are set as d = 72, m = [6, 4], w = [7, 5] for the Weibo dataset, and d = 56, m = [6, 4], w = [7, 5] for Twitter dataset (m, w are the numbers of feature maps and filter width of two layers). |