Notice: 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. Full accuracy metrics and methodology are described in [1].
External Reliable Information-enhanced Multimodal Contrastive Learning for Fake News Detection
Authors: Biwei Cao, Qihang Wu, Jiuxin Cao, Bo Liu, Jie Gui
AAAI 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments are done on two commonly used datasets in different languages, X (Twitter) and Weibo. Experiment results demonstrate that our proposed model ERICFND outperforms existing state-of-the-art fake news detection methods under the same settings. |
| Researcher Affiliation | Academia | 1 School of Cyber Science and Engineering, Southeast University, Nanjing 211189, China 2 Key Laboratory of Computer Network and Information of Ministry of Education of China, Nanjing 211189, China 3 Purple Mountain Laboratories, Nanjing 210000, China 4 School of Computer Science and Engineering, Southeast University, Nanjing 211189, China 5 Engineering Research Center of Blockchain Application, Supervision And Management (Southeast University), Ministry of Education EMAIL |
| Pseudocode | No | The paper describes the methodology steps using natural language and mathematical equations, without including any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Extended version and code https://github.com/Tarasom123/ERIC-FND |
| Open Datasets | Yes | In this paper, we evaluate the proposed model ERIC-FND using the two widely used datasets Weibo and X. Weibo dataset is constructed by Jin et al. (2017a) with the fake news in the dataset from misinformation collection by Weibo official from May 2012 to January 2016. ... X dataset is used in competition Media Eval (Boididou et al. 2015) for automatically detecting fake news in various media formats on X. |
| Dataset Splits | Yes | The remaining data are split into a training set and a test set in an 8:2 ratio. For the condition that one news piece related to multiple images, only the first image is selected. As shown in Table 1, the dataset consists of 9,523 news pieces, with 7,528 in the training set and 1,925 in the test set. ... As shown in Table 2, the dataset has 12,514 news pieces, with 11,252 in the training set and 1,262 in the test set. |
| Hardware Specification | Yes | The main parameters of the server computing environment are shown as follows: CPU: AMD EPYC 7642 CPU @ 3293MHz 48C96T; GPU: NVIDIA A100-SXM4-80GB; CUDA Version: 11.0; Memory: 32GB DDR4 2666MHz ECC 16; Operating System: Cent OS Linux 7 (Core) Linux 3.10.0. |
| Software Dependencies | Yes | CUDA Version: 11.0 |
| Experiment Setup | No | The settings of used parameters and the implementation details are described in the Appendix Section. |