Details
| Original language | English |
|---|---|
| Title of host publication | IJCNN 2021 - International Joint Conference on Neural Networks, Proceedings |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Number of pages | 8 |
| ISBN (electronic) | 9780738133669 |
| ISBN (print) | 978-1-6654-4597-9 |
| Publication status | Published - 18 Jul 2021 |
| Event | 2021 International Joint Conference on Neural Networks, IJCNN 2021 - Virtual, Shenzhen, China Duration: 18 Jul 2021 → 22 Jul 2021 |
Publication series
| Name | Proceedings of the International Joint Conference on Neural Networks |
|---|---|
| Volume | 2021-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
- Computer Science(all)
- Software
- Computer Science(all)
- Artificial Intelligence
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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 proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - MulCoB-MulFaV
T2 - 2021 International Joint Conference on Neural Networks, IJCNN 2021
AU - Roy, Arjun
AU - Ekbal, Asif
PY - 2021/7/18
Y1 - 2021/7/18
N2 - 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.
AB - 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.
KW - fact verification
KW - multi-lingual
KW - multimodal
UR - http://www.scopus.com/inward/record.url?scp=85116430661&partnerID=8YFLogxK
U2 - 10.1109/IJCNN52387.2021.9533916
DO - 10.1109/IJCNN52387.2021.9533916
M3 - Conference contribution
AN - SCOPUS:85116430661
SN - 978-1-6654-4597-9
T3 - Proceedings of the International Joint Conference on Neural Networks
BT - IJCNN 2021 - International Joint Conference on Neural Networks, Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 18 July 2021 through 22 July 2021
ER -