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A Comprehensive Survey of Multimodal Fake News Detection: Datasets, Methods, and Challenges

  • Guoyong Cai 1,2,*,   
  • Zhipeng Qiu 1,   
  • Guoxin Bi 1,   
  • Qinghua Liu 1,2

Received: 04 Feb 2026 | Revised: 27 Mar 2026 | Accepted: 03 Apr 2026 | Published: 12 May 2026

Abstract

With the rapid proliferation of multimodal social media platforms, fake news has been increasingly disseminated through multiple modalities such as text, images, and videos, posing serious threats to social stability, public cognition, and these platforms’ ecosystem. Existing unimodal fake news detection methods face much challenge in multimodal scenarios, as they can not capture fully cross-modal semantic correlations and inconsistencies. Multimodal fake news detection, which integrates heterogeneous information from text, visual, and audio modalities to explore inter-modal consistency and complementarity, has therefore become a major research focus in recent years. A comprehensive survey of recent advances in multimodal fake news detection is presented in the paper, which consists of systematically reviews of the fundamental concepts, detection tasks, and underlying technical principles in this field. Major benchmark datasets and commonly used evaluation metrics are also introduced, followed by a structured taxonomy of representative detection methods and a summary of their experimental results. Furthermore, the key challenges faced by current research are discussed and promising future research directions are outlined. Compared with existing surveys, this work presents a more comprehensive method categorization that emphasizes the evolution of detection techniques, offers clearer comparisons of datasets and experimental analyses, and provides more practical insights for researchers and practitioners in multimodal fake news detection.

References 

  • 1.

    Li, X.; Qiao, J.; Yin, S.; et al. A Survey of Multimodal Fake News Detection: A Cross-Modal Interaction Perspective. IEEE Trans. Emerg. Top. Comput. Intell. 2025, 9, 2658–2675.

  • 2.

    Alghamdi, J.; Luo, S.; Lin, Y. A comprehensive survey on machine learning approaches for fake news detection. Multimed. Tools Appl. 2024, 83, 51009–51067.

  • 3.

    Nasser, M.; Arshad, N.I.; Ali, A.; et al. A Systematic Review of Multimodal Fake News Detection on Social Media Using Deep Learning Models. Results Eng. 2025, 26, 104752.

  • 4.

    Yang, S.; Li, X.; Du, Y. A Survey of Fake News Detection Methods Based on Online Social Networks. J. Xihua Univ. 2025, 44, 37–46. (in Chinese).

  • 5.

    Tufchi, S.; Yadav, A.; Ahmed, T. A Comprehensive Survey of Multimodal Fake News Detection Techniques: Advances, Challenges, and Opportunities. Int. J. Multimed. Inf. Retr. 2023, 12, 28.

  • 6.

    Ma, J.; Gao, W.; Mitra, P.; et al. Detecting Rumors from Microblogs with Recurrent Neural Networks. In Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence (IJCAI), New York, NY, USA, 9–15 July 2016; pp. 3818–3824.

  • 7.

    Zhang, X.Y.; Cao, J.; Li, X.R.; et al. Mining Dual Emotion for Fake News Detection. In Proceedings of the Web Conference 2021, Ljubljana, Slovenia, 19–23 April 2021; pp. 3465–3476.

  • 8.

    Nan, Q.; Cao, J.; Zhu, Y.C.; et al. MDFEND: Multi-Domain Fake News Detection. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management (CIKM), Gold Coast, QLD, Australia, 1–5 November 2021; pp. 3343–3347.

  • 9.

    Jin, Z.; Cao, J.; Guo, H.; et al. Multimodal Fusion with Recurrent Neural Networks for Rumor Detection on Microblogs. In Proceedings of the 25th ACM International Conference on Multimedia, Mountain View, CA, USA, 23–27 October 2017; pp. 795–816.

  • 10.

    Zubiaga, A.; Liakata, M.; Procter, R. Exploiting Context for Rumor Detection in Social Media. In Proceedings of the International Conference on Social Informatics (SocInfo), Oxford, UK, 13–15 September 2017; pp. 109–123.

  • 11.

    Ma, J.; Gao, W.; Wong, K.F. Detect Rumors in Microblog Posts Using Propagation Structure via Kernel Learning. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (ACL), Vancouver, BC, Canada, 30 July–4 August 2017; pp. 708–717.

  • 12.

    Boididou, C.; Papadopoulos, S.; Zampoglou, M.; et al. Detection and Visualization of Misleading Content on Twitter. Int. J. Multimed. Inf. Retr. 2018, 7, 71–86.

  • 13.

    Wu, Y.; Tang, Y.; Fan, C.; et al. MDVT: A Multimodal Fake News Detection Framework Based on Vision Transformer. In Proceedings of the 2023 6th International Conference on Machine Learning and Natural Language Processing, Sanya China, 27–29 December 2023.

  • 14.

    Shu, K.; Mahudeswaran, D.; Wang, S.H.; et al. FakeNewsNet: A Data Repository with News Content, Social Context, and Spatiotemporal Information for Studying Fake News on Social Media. Big Data 2020, 8, 171–188.

  • 15.

    Qi, P.; Bu, Y.Y.; Cao, J.; et al. FakeSV: A Multimodal Benchmark with Rich Social Context for Fake News Detection on Short Video Platforms. In Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence (AAAI), Washington, DC, USA, 7–14 February 2023; pp. 14444–14452.

  • 16.

    Wang, W.Y. “Liar, Liar Pants on Fire”: A New Benchmark Dataset for Fake News Detection. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (ACL), Vancouver, BC, Canada, 30 July–4 August 2017; pp. 422–426.

  • 17.

    Li, Y.; Jiang, B.; Shu, K.; et al. Toward a Multilingual and Multimodal Data Repository for COVID‑19 Disinformation. In Proceedings of the 2020 IEEE International Conference on Big Data (Big Data), Online, 10–13 December 2020; pp. 4325–4330.

  • 18.

    Popat, K.; Mukherjee, S.; Strötgen, J.; et al. Where the Truth Lies: Explaining the Credibility of Emerging Claims on the Web and Social Media. In Proceedings of the 26th International Conference on World Wide Web Companion (WWW Companion), Perth, WA, Australia, 3–7 April 2017; pp. 1003–1012.

  • 19.

    Nakamura, K.; Levy, S.; Wang, W.Y. Fakeddit: A New Multimodal Benchmark Dataset for Fine‑Grained Fake News Detection. In Proceedings of the Twelfth Language Resources and Evaluation Conference (LREC), Marseille, France, 11–16 May 2020; pp. 6149–6157.

  • 20.

    Liu, H.; Chen, S.; Cao, S.; et al. Fake News Detection Based on Multimodal Learning. Comput. Sci. Explor. 2023, 17, 2015–2029.

  • 21.

    Cantini, R.; Cosentino, C.; Kilanioti, I.; et al. Unmasking Deception: A Topic‑Oriented Multimodal Approach to Uncover False Information on Social Media. Mach. Learn. 2025, 114, 13.

  • 22.

    Farhoudinia, B.; Ozturkcan, S.; Kasap, N. Emotions Unveiled: Detecting COVID‑19 Fake News on Social Media. Humanit. Soc. Sci. Commun. 2024, 11, 932.

  • 23.

    Qian, L.; Xu, R.; Zhou, Z. MRDCA: A Multimodal Approach for Fine‑Grained Fake News Detection through Integration of RoBERTa and DenseNet Based upon Fusion Mechanism of Co‑Attention. Ann. Oper. Res. 2025, 348, 257–278.

  • 24.

    Chen, W.; Cai, F.; Guo, Y.; et al. Contrastive Learning of Cross‑Modal Information Enhancement for Multimodal Fake News Detection. Complex Intell. Syst. 2025, 11, 303.

  • 25.

    Shen, X.; Huang, M.; Hu, Z.; et al. Multimodal Fake News Detection with Contrastive Learning and Optimal Transport. Front. Comput. Sci. 2024, 6, 1473457.

  • 26.

    Xing, Y.; Zhai, C.; Che, Z.; et al. A Multimodal Fake News Detection Model Based on Bidirectional Semantic Enhancement and Adversarial Network Under Web3.0. Electronics 2025, 14, 3652.

  • 27.

    Glenski, M.; Weninger, T.; Volkova, S. Propagation from Deceptive News Sources: Who Shares, How Much, How Evenly, and How Quickly? IEEE Trans. Comput. Soc. Syst. 2018, 5, 1071–1082.

  • 28.

    Li, P.; Sun, X.; Yu, H.; et al. Entity‑Oriented Multi‑Modal Alignment and Fusion Network for Fake News Detection. IEEE Trans. Multimed. 2022, 24, 3455–3468.

  • 29.

    Qi, P.; Zhao, Y.; Shen, Y.; et al. Two Heads Are Better than One: Improving Fake News Video Detection by Correlating with Neighbors. In Findings of the Association for Computational Linguistics: ACL 2023, Toronto, ON, Canada, 9–14 July 2023; pp. 11947–11959.

  • 30.

    Zhang, Y.; Su, X.; Wu, J.; et al. EmoKnow: Emotion‑ and Knowledge‑Oriented Model for COVID‑19 Fake News Detection. In Proceedings of the 19th International Conference on Advanced Data Mining and Applications (ADMA), Cham, Switzerland, 2023; pp. 352–367.

  • 31.

    Xie, B.; Ma, X.; Wu, J.; et al. Knowledge Graph Enhanced Heterogeneous Graph Neural Network for Fake News Detection. IEEE Trans. Consum. Electron. 2023, 70, 2826–2837.

  • 32.

    Hao, R.; Luo, H.; Li, Y. Multi‑Modal Fake News Detection Enhanced by Fine‑Grained Knowledge Graph. IEICE Trans. Inf. Syst. 2024, E107‑D, 1234–1245.

  • 33.

    Hu, L.; Yang, T.; Zhang, L.; et al. Compare to the Knowledge: Graph Neural Fake News Detection with External Knowledge. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (ACL‑IJCNLP), Online, 1–6 August 2021; pp. 754–763.

  • 34.

    Fu, L.; Liu, S. Multimodal Fake News Detection Incorporating External Knowledge and User Interaction Feature. Adv. Multimed. 2023, 2023, 8836476.

  • 35.

    Han, M.; Li, J.; Chen, Y.; et al. EC‑Fake: A Fake News Detection Model Based on External Knowledge and Contrast‑Driven Feature Augmentation. Neurocomputing 2025, 132, 131214.

  • 36.

    Yu, W.; Ge, J.; Chen, Z.; et al. Research on Fake News Detection Based on Dual Evidence Perception. Eng. Appl. Artif. Intell. 2024, 133, 108271.

  • 37.

    Cheung, T.H.; Lam, K.M. FactLLaMA: Optimizing Instruction‑Following Language Models with External Knowledge for Automated Fact‑Checking. In Proceedings of the Asia Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), Tokyo, Japan, 31 October–3 November 2023; pp. 846–853.

  • 38.

    Zheng, L.; Li, C.; Zhang, X.; et al. Evidence Retrieval Is Almost All You Need for Fact Verification. In Proceedings of the Findings of the Association for Computational Linguistics: ACL 2024, Bangkok, Thailand, 11–16 August 2024; pp. 9274–9281.

  • 39.

    Wu, L.; Wang, K.; Nie, K.; et al. TFGIN: Tight‑Fitting Graph Inference Network for Table‑Based Fact Verification. ACM Trans. Inf. Syst. 2025, 43, 1–26.

  • 40.

    Wu, L.; Wang, L.; Zhao, Y. Unified Evidence Enhancement Inference Framework for Fake News Detection. In Proceedings of the 33rd International Joint Conference on Artificial Intelligence (IJCAI 2024), Jeju, South Korea, 3–9 August 2024; pp. 6541–6549.

  • 41.

    Wu, L.; Yu, D.; Liu, P.; et al. Heuristic Heterogeneous Graph Reasoning Networks for Fact Verification. IEEE Trans. Neural Netw. Learn. Syst. 2023, 35, 14959–14973.

  • 42.

    Dellys, H.N.; Mokeddem, H.; Sliman, L. On the Integration of Social Context for Enhanced Fake News Detection Using Multimodal Fusion Attention Mechanism. AI 2025, 6, 78.

  • 43.

    Raza, S.; Ding, C. Fake News Detection Based on News Content and Social Contexts: A Transformer‑Based Approach. Int. J. Data Sci. Anal. 2022, 13, 335–362.

  • 44.

    Mehta, N.; Pacheco, M.L.; Goldwasser, D. Tackling Fake News Detection by Continually Improving Social Context Representations Using Graph Neural Networks. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (ACL), Dublin, Ireland, 22–27 May 2022; pp. 1363–1380.

  • 45.

    Su, X.; Yang, J.; Wu, J.; et al. Hy‑DeFake: Hypergraph Neural Networks for Detecting Fake News in Online Social Networks. Neural Netw. 2025, 187, 107302.

  • 46.

    Malik, A.; Behera, D.K.; Hota, J.; et al. Ensemble Graph Neural Networks for Fake News Detection Using User Engagement and Text Features. Results Eng. 2024, 24, 103081.

  • 47.

    Lakzaei, B.; Chehreghani, M.H.; Bagheri, A. LOSS‑GAT: Label Propagation and One‑Class Semi‑Supervised Graph Attention Network for Fake News Detection. Appl. Soft Comput. 2025, 174, 112965.

  • 48.

    Lan, W.; Cheung, Y.; Xu, Q.; et al. Improve Knowledge Distillation via Label Revision and Data Selection. IEEE Trans. Cogn. Dev. Syst. 2025, 17, 1377–1388.

  • 49.

    Mu, M.; Das Bhattacharjee, S.; Yuan, J. Self‑Supervised Distilled Learning for Multi‑Modal Misinformation Identification. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), Waikoloa, HI, USA, 2–7 January 2023; pp. 2819–2828.

  • 50.

    Chen, X.; Huang, X.; Gao, Q.; et al. Enhancing Text‑Centric Fake News Detection via External Knowledge Distillation from LLMs. Neural Netw. 2025, 187, 107377.

  • 51.

    Chen, Z.; Hu, L.; Li, W.; et al. Causal Intervention and Counterfactual Reasoning for Multi‑Modal Fake News Detection. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (ACL), Volume 1: Long Papers, Toronto, ON, Canada, 9–14 July 2023; pp. 627–638.

  • 52.

    Gong, S.; Sinnott, R.; Qi, J.; et al. Unseen Fake News Detection through Causal Debiasing. In Proceedings of the ACM on Web Conference 2025 (WWW); Sydney, NSW, Australia, 28 April–2 May 2025; pp. 981–985.

  • 53.

    Cheng, L.; Guo, R.; Shu, K.; et al. Causal Understanding of Fake News Dissemination on Social Media. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), Singapore, 14–18 August 2021; ACM: New York, NY, USA, 2021; pp. 148–157.

  • 54.

    Hu, B.; Sheng, Q.; Cao, J.; et al. Bad Actor, Good Advisor: Exploring the Role of Large Language Models in Fake News Detection. In Proceedings of the AAAI Conference on Artificial Intelligence, Vancouver, BC, Canada, 20–27 February 2024; AAAI Press: Palo Alto, CA, USA, 2024; pp. 22105–22113.

  • 55.

    Liu, Y.; Zhu, J.; Liu, X.; et al. Detect, Investigate, Judge and Determine: A Knowledge‑Guided Framework for Few‑Shot Fake News Detection. In Proceedings of the 2025 IEEE International Conference on Data Mining (ICDM), Washington DC, DC, USA, 12–15 November 2025; pp. 477–486.

  • 56.

    Chalehchaleh, R.; Farahbakhsh, R.; Crespi, N. Addressing Data Scarcity in Multilingual Fake News Detection: An LLM‑Based Dataset Augmentation Approach. Soc. Netw. Anal. Min. 2025, 15, 92.

  • 57.

    Li, D.; Li, F.; Song, B.B.; et al. IMRRF: Integrating Multi‑Source Retrieval and Redundancy Filtering for LLM‑Based Fake News Detection. In Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies, Miami, FL, USA, 10–15 June 2025; pp. 9127–9142.

  • 58.

    Li, H.; Huang, J.; Ji, M.; et al. Use of Retrieval‑Augmented Large Language Model for COVID‑19 Fact‑Checking: Development and Usability Study. J. Med. Internet Res. 2025, 27, e66098.

  • 59.

    Khaliq, M.A.; Chang, P.Y.C.; Ma, M.; et al. RAGAR, Your Falsehood Radar: RAG‑Augmented Reasoning for Political Fact‑Checking Using Multimodal Large Language Models. In Proceedings of the Seventh Fact Extraction and VERification Workshop (FEVER), Miami, FL, USA, 16–21 June 2024; pp. 280–296.

  • 60.

    Deng, X.; Yu, P.; Qian, S.; et al. Denoising‑Enhanced Multimodal Detection Network for Fake News Video Detection. IEEE Trans. Circuits Syst. Video Technol. 2026. https://doi.org/10.1109/TCSVT.2026.3672169.

  • 61.

    Zhang, C.; Feng, Z.; Zhang, Z.; et al. Is LLMs Hallucination Usable? LLM‑Based Negative Reasoning for Fake News Detection. In Proceedings of the AAAI Conference on Artificial Intelligence, Philadelphia, PA, USA, 25 February–4 March 2025; Volume 39, pp. 1031–1039.

  • 62.

    Liu, H.; Wang, W.; Li, H.; et al. TELLER: A Trustworthy Framework for Explainable, Generalizable and Controllable Fake News Detection. In Proceedings of the Findings of the Association for Computational Linguistics: ACL 2024, Bangkok, Thailand, 11–16 August 2024; pp. 15556–15583.

  • 63.

    Zheng, X.; Zeng, Z.; Wang, H.; et al. From Predictions to Analyses: Rationale‑Augmented Fake News Detection with Large Vision‑Language Models. In Proceedings of the ACM on Web Conference 2025, Sydney, NSW, Australia, 28 April–2 May 2025; pp. 5364–5375.

  • 64.

    Wang, Y.; Ma, F.; Jin, Z.; et al. EANN: Event Adversarial Neural Networks for Multi‑Modal Fake News Detection. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, London, UK, 19–23 August 2018; pp. 849–857.

  • 65.

    Khattar, D.; Goud, J.S.; Gupta, M.; et al. MVAE: Multimodal Variational Autoencoder for Fake News Detection. In Proceedings of the World Wide Web Conference, San Francisco, CA, USA, 13–17 May 2019; pp. 2915–2921.

  • 66.

    Singhal, S.; Shah, R.R.; Chakraborty, T.; et al. SpotFake: A Multi‑Modal Framework for Fake News Detection. In Proceedings of the 2019 IEEE Fifth International Conference on Multimedia Big Data, Singapore, 11–13 September 2019; pp. 39–47.

  • 67.

    Chen, Y.; Li, D.; Zhang, P.; et al. Cross‑Modal Ambiguity Learning for Multimodal Fake News Detection. In Proceedings of the ACM Web Conference 2022, Lyon, France, 25–29 April 2022; pp. 2897–2905.

  • 68.

    Wu, Y.; Zhan, P.; Zhang, Y.; et al. Multimodal Fusion with Co‑Attention Networks for Fake News Detection. In Proceedings of the Findings of the Association for Computational Linguistics: ACL‑IJCNLP 2021, Online, 1–6 August 2021; pp. 2560–2569.

  • 69.

    Qian, S.; Wang, J.; Hu, J.; et al. Hierarchical Multi‑Modal Contextual Attention Network for Fake News Detection. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, Virtual Event, 11–15 July 2021; pp. 153–162.

  • 70.

    Singhal, S.; Pandey, T.; Mrig, S.; et al. Leveraging Intra‑ and Inter‑Modality Relationship for Multimodal Fake News Detection. In Companion Proceedings of the Web Conference 2022, Virtual Event, 25–29 April 2022; pp. 726–734.

  • 71.

    Wu, L.; Liu, P.; Zhao, Y.; et al. Human Cognition‑Based Consistency Inference Networks for Multi‑Modal Fake News Detection. IEEE Trans. Knowl. Data Eng. 2023, 36, 211–225.

  • 72.

    Zhou, Y.; Yang, Y.; Ying, Q.; et al. Multi‑Modal Fake News Detection on Social Media via Multi‑Grained Information Fusion. In Proceedings of the 2023 ACM International Conference on Multimedia Retrieval, Thessaloniki, Greece, 12–15 June 2023; pp. 343–352.

  • 73.

    Lao, A.; Zhang, Q.; Shi, C.; et al. Frequency Spectrum Is More Effective for Multimodal Representation and Fusion: A Multimodal Spectrum Rumor Detector. In Proceedings of the AAAI Conference on Artificial Intelligence, Vancouver, BC, Canada, 20–27 February 2024; pp. 18426–18434.

  • 74.

    Zheng, X.; Luo, M.; Wang, X. Unveiling Fake News with Adversarial Arguments Generated by Multimodal Large Language Models. In Proceedings of the 31st International Conference on Computational Linguistics, Abu Dhabi, United Arab Emirates, 19–24 January 2025; pp. 7862–7869.

  • 75.

    Wu, L.; Long, Y.; Gao, C.; et al. MFIR: Multimodal Fusion and Inconsistency Reasoning for Explainable Fake News Detection. Inf. Fusion 2023, 100, 101944.

  • 76.

    Shao, Z.; Cai, G.; Liu, Q.; et al. An Active Learning Framework for Continuous Rapid Rumor Detection in Evolving Social Media. In Proceedings of the 2024 International Joint Conference on Neural Networks, Yokohama, Japan, 30 June–5 July 2024; pp. 1–8.

  • 77.

    Suryawanshi, S.; Goswami, A.; Patil, P. FakeIDCA: Fake News Detection with Incremental Deep Learning Based Concept Drift Adaption. Multimed. Tools Appl. 2024, 83, 28579–28594.

  • 78.

    Jayakody, N.; Mohammad, A.; Halgamuge, M.N. Fake News Detection Using a Decentralized Deep Learning Model and Federated Learning. In Proceedings of the IECON 2022—48th Annual Conference of the IEEE Industrial Electronics Society, Brussels, Belgium, 17–20 October 2022; pp. 1–6.

  • 79.

    Ye, Y.; Feng, X.; Yuan, Z.; et al. CC‑TUNING: A Cross‑Lingual Connection Mechanism for Improving Joint Multilingual Supervised Fine‑Tuning. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics, Vienna, Austria, 10–15 August 2025; pp. 19036–19051.

  • 80.

    Han, L.; Zhang, X.; Zhou, Z.; et al. A Multifaceted Reasoning Network for Explainable Fake News Detection. Inf. Process. Manag. 2024, 61, 103822.

  • 81.

    Yan, F.; Zhang, M.; Wei, B.; et al. SARD: Fake News Detection Based on CLIP Contrastive Learning and Multimodal Semantic Alignment. J. King Saud Univ. Comput. Inf. Sci. 2024, 36, 102160.

  • 82.

    Jin, Y.; Wang, X.; Yang, R.; et al. Towards Fine‑Grained Reasoning for Fake News Detection. In Proceedings of the AAAI Conference on Artificial Intelligence, Virtual Event, 22 February–1 March 2022; pp. 5746–5754.

  • 83.

    Wang, Z.; Pan, S.; Yang, Z. DBPR: Dynamic Bidirectional Propagation Relationship Graph Convolution Network for Fake News Detection on Social Media. In Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics, Honolulu, HI, USA, 1–4 October 2023; pp. 254–260.

  • 84.

    Wei, Z.; Pan, H.; Qiao, L.; et al. Cross‑Modal Knowledge Distillation in Multi‑Modal Fake News Detection. In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, Singapore, 23–27 May 2022; pp. 4733–4737.

  • 85.

    Habib, M.A.; Wadud, M.A.H.; Mridha, M.F.; et al. LLM‑Powered Multimodal Reasoning for Fake News Detection. Comput. Mater. Contin. 2026, 87, 77.

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DOI
10.53941/tai.2026.100009
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Volume 2, Issue 1
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Cai, G.; Qiu, Z.; Bi, G.; Liu, Q. A Comprehensive Survey of Multimodal Fake News Detection: Datasets, Methods, and Challenges. Transactions on Artificial Intelligence 2026, 2 (1), 131–160. https://doi.org/10.53941/tai.2026.100009.
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