Reinforced Adaptive Knowledge Learning for Multimodal Fake News Detection
Authors: Litian Zhang, Xiaoming Zhang, Ziyi Zhou, Feiran Huang, Chaozhuo Li
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our proposal is extensively evaluated over three popular datasets, and experimental results demonstrate the superiority of AKA-Fake. ... Our proposal consistently outperforms all baselines over three datasets, achieving +4.7% on Politi Fact, +2.4% on Gossip Cop, and +2.8% on Pheme in terms of accuracy. The superiority of AKA-Fake verifies the effectiveness of adaptive knowledge incorporation. ... In this section, ablation studies are conducted to investigate the importance of different modules. |
| Researcher Affiliation | Academia | 1 Beihang University, 2Jinan University, 3Beijing University of Posts and Telecommunications |
| Pseudocode | No | The paper describes various algorithms and processes (e.g., reinforcement learning, graph refinement), but it does not include any pseudocode blocks or sections explicitly labeled as 'Pseudocode' or 'Algorithm'. |
| Open Source Code | No | The paper does not contain any explicit statements about releasing source code for the proposed methodology or provide links to a code repository. |
| Open Datasets | Yes | We conduct experiments on three popular datasets: Politi Fact, Gossip Cop (Shu et al. 2020), and Pheme (Qi et al. 2019), which contain 495, 15,707 and 2,099 news, respectively. |
| Dataset Splits | Yes | The dataset is randomly split into training, validation, and testing sets with a ratio of 7:1:2. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU models, CPU types, memory) used to run the experiments. |
| Software Dependencies | No | The paper mentions using 'CLIP (Radford et al. 2021)' and 'Adam (Kingma and Ba 2014) as the optimizer' but does not specify version numbers for these or other software libraries/dependencies. |
| Experiment Setup | Yes | The respective field size is set to [5, 3, 2]. We employ Adam (Kingma and Ba 2014) as the optimizer. The batch size is set as 64. The initial learning rate is set to 2e 4. The embedding size for entities, relations, and document is 1024. |