MMM: An Emotion and Novelty-aware Approach for Multilingual Multimodal Misinformation Detection

Vipin Gupta, Rina Kumari, Nischal Ashok, Tirthankar Ghosal, Asif Ekbal

Abstract
The growth of multilingual web content in low-resource languages is becoming an emerging challenge to detect misinformation. One particular hindrance to research on this problem is the non-availability of resources and tools. Majority of the earlier works in misinformation detection are based on English content which confines the applicability of the research to a specific language only. Increasing presence of multimedia content on the web has promoted misinformation in which real multimedia content (images, videos) are used in different but related contexts with manipulated texts to mislead the readers. Detecting this category of misleading information is almost impossible without any prior knowledge. Studies say that emotion-invoking and highly novel content accelerates the dissemination of false information. To counter this problem, here in this paper, we first introduce a novel multilingual multimodal misinformation dataset that includes background knowledge (from authentic sources) of the misleading articles. Second, we propose an effective neural model leveraging novelty detection and emotion recognition to detect fabricated information. We perform extensive experiments to justify that our proposed model outperforms the state-of-the-art (SOTA) on the concerned task.
Anthology ID:
2022.findings-aacl.43
Volume:
Findings of the Association for Computational Linguistics: AACL-IJCNLP 2022
Month:
November
Year:
2022
Address:
Online only
Editors:
Yulan He, Heng Ji, Sujian Li, Yang Liu, Chua-Hui Chang
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
464–477
Language:
URL:
https://aclanthology.org/2022.findings-aacl.43
DOI:
Bibkey:
Cite (ACL):
Vipin Gupta, Rina Kumari, Nischal Ashok, Tirthankar Ghosal, and Asif Ekbal. 2022. MMM: An Emotion and Novelty-aware Approach for Multilingual Multimodal Misinformation Detection. In Findings of the Association for Computational Linguistics: AACL-IJCNLP 2022, pages 464–477, Online only. Association for Computational Linguistics.
Cite (Informal):
MMM: An Emotion and Novelty-aware Approach for Multilingual Multimodal Misinformation Detection (Gupta et al., Findings 2022)
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