Towards Cross-Lingual Multi-Modal Misinformation Detection for E-Commerce Management
Article
He, Yifan, Li, Zhao, Li, Zhenpeng, Zhou, Shuigeng, Yu, Ting and Zhang, Ji. 2023. "Towards Cross-Lingual Multi-Modal Misinformation Detection for E-Commerce Management." IEEE Transactions on Network and Service Management. 20 (2), pp. 1040-1050. https://doi.org/10.1109/TNSM.2023.3234114
Article Title | Towards Cross-Lingual Multi-Modal Misinformation Detection for E-Commerce Management |
---|---|
ERA Journal ID | 5064 |
Article Category | Article |
Authors | He, Yifan, Li, Zhao, Li, Zhenpeng, Zhou, Shuigeng, Yu, Ting and Zhang, Ji |
Journal Title | IEEE Transactions on Network and Service Management |
Journal Citation | 20 (2), pp. 1040-1050 |
Number of Pages | 11 |
Year | 2023 |
Publisher | IEEE (Institute of Electrical and Electronics Engineers) |
Place of Publication | United States |
ISSN | 1932-4537 |
Digital Object Identifier (DOI) | https://doi.org/10.1109/TNSM.2023.3234114 |
Web Address (URL) | https://ieeexplore.ieee.org/abstract/document/10005829 |
Abstract | The misinformation detection systems are increasingly important in E-commerce management, which detect misinformation on the commodity display page. Misinformation in E-commerce is usually presented as a mismatch between multi-modal information, the detection systems need to find the misinformation across the multi-model information. Furthermore, with the development of E-commerce globalization, we hope to deploy the system in the international E-commerce platform, which may face the difficulty caused by multi-lingual data. To this end, we propose a C ross-lingual M ulti-modal M isinformation D etection (CMMD) framework for E-commerce management. The CMMD framework includes a word alignment network that embeds information from different languages into the same feature space and a multimodal fusion structure that fuses text representations and image representations through two self-supervised tasks. With the cooperation of these two modules, the CMMD model could extract rich cross-lingual multi-modal features to achieve accurate misinformation detection. We conduct experiments on the public multi-modal dataset and further apply the proposed CMMD to the real-world E-commerce international platform. The experimental results show that the proposed framework CMMD achieves better performance on the public dataset than some benchmarks and gets satisfactory results in real-world E-commerce management. |
Keywords | Cross-lingual; multi-modal; word alignment; misinformation detection |
ANZSRC Field of Research 2020 | 460299. Artificial intelligence not elsewhere classified |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | Fudan University, China |
Zhejiang University, China | |
Alibaba Group, China | |
Zhejiang Lab, China | |
University of Southern Queensland |
Permalink -
https://research.usq.edu.au/item/z25yq/towards-cross-lingual-multi-modal-misinformation-detection-for-e-commerce-management
56
total views0
total downloads3
views this month0
downloads this month