MulCoB-MulFaV: Multimodal Content Based Multilingual Fact Verification

Research output: Chapter in book/report/conference proceedingConference contributionResearchpeer review

Authors

  • Arjun Roy
  • Asif Ekbal

Research Organisations

External Research Organisations

  • Indian Institute of Technology Patna (IITP)
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Details

Original languageEnglish
Title of host publicationIJCNN 2021 - International Joint Conference on Neural Networks, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages8
ISBN (electronic)9780738133669
ISBN (print)978-1-6654-4597-9
Publication statusPublished - 18 Jul 2021
Event2021 International Joint Conference on Neural Networks, IJCNN 2021 - Virtual, Shenzhen, China
Duration: 18 Jul 202122 Jul 2021

Publication series

NameProceedings of the International Joint Conference on Neural Networks
Volume2021-July

Abstract

Verifying fact of multimodal (text, image and/or videos) reports is emerging as an important challenge to prevent circulation of fake news reports in various online platform. Moreover, such reports often being posted in local languages penetrate even faster, making the problem more complex. To tackle such problem many fact verifying web sites have emerged, which deploys humans to manually find the truthfulness of such reports with the help of multimodal contents that are being used in such report. But in recent times, due to diversity of the problem owing to various political, and malicious motives, the existing (manual) system fails to handle the enormity. Existing content based approaches rely only on textual content of such reports and fail to handle the multimodal nature. On the other hand, existing multimodal based approaches rely only on the report based and user based features, and fail to interpret the fake contents with evidences. In this work, we propose a novel end-to-end automated multimodal content based multilingual fact verification system, which automates the task of fact verifying web sites, and provides evidences for every judgment. Evaluation results on three benchmark datasets show the robustness and effectiveness of our approach.

Keywords

    fact verification, multi-lingual, multimodal

ASJC Scopus subject areas

Cite this

MulCoB-MulFaV: Multimodal Content Based Multilingual Fact Verification. / Roy, Arjun; Ekbal, Asif.
IJCNN 2021 - International Joint Conference on Neural Networks, Proceedings. Institute of Electrical and Electronics Engineers Inc., 2021. (Proceedings of the International Joint Conference on Neural Networks; Vol. 2021-July).

Research output: Chapter in book/report/conference proceedingConference contributionResearchpeer review

Roy, A & Ekbal, A 2021, MulCoB-MulFaV: Multimodal Content Based Multilingual Fact Verification. in IJCNN 2021 - International Joint Conference on Neural Networks, Proceedings. Proceedings of the International Joint Conference on Neural Networks, vol. 2021-July, Institute of Electrical and Electronics Engineers Inc., 2021 International Joint Conference on Neural Networks, IJCNN 2021, Virtual, Shenzhen, China, 18 Jul 2021. https://doi.org/10.1109/IJCNN52387.2021.9533916
Roy, A., & Ekbal, A. (2021). MulCoB-MulFaV: Multimodal Content Based Multilingual Fact Verification. In IJCNN 2021 - International Joint Conference on Neural Networks, Proceedings (Proceedings of the International Joint Conference on Neural Networks; Vol. 2021-July). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IJCNN52387.2021.9533916
Roy A, Ekbal A. MulCoB-MulFaV: Multimodal Content Based Multilingual Fact Verification. In IJCNN 2021 - International Joint Conference on Neural Networks, Proceedings. Institute of Electrical and Electronics Engineers Inc. 2021. (Proceedings of the International Joint Conference on Neural Networks). doi: 10.1109/IJCNN52387.2021.9533916
Roy, Arjun ; Ekbal, Asif. / MulCoB-MulFaV : Multimodal Content Based Multilingual Fact Verification. IJCNN 2021 - International Joint Conference on Neural Networks, Proceedings. Institute of Electrical and Electronics Engineers Inc., 2021. (Proceedings of the International Joint Conference on Neural Networks).
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