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Beyond just saying it’s false: explainable AI for multimodal misinformation detection

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Abstract

The demand for explainable AI in misinformation detection is crucial for building user trust and understanding model behavior. Many recent methods try to explain how they spot fake news using text, images, or both (multimodal). However, these methods often rely on fixed-size explanations (text and images) generated through ranking-based systems, which fail to effectively differentiate between explainable and non-explainable components. This shortcoming results in vague explanations and limited model performance. To overcome these aforesaid issues, we come up with a multimodal EXplainable misinformation detection method based on ACute Thresholding mechanism (mEXACT) that identifies a variable-size bucket of check-worthy information, when removed, can flip the model’s prediction from fake to real. Identifying minimal set of significant information enables our model to distinguish between contributing and non-contributing misinformation components, thereby enhancing interpretability while improving classification performance. Extensive experiments on two real-world multimodal COVID-19 misinformation datasets, ReCOVery and MMCoVaR, demonstrate that mEXACT significantly outperforms state-of-the-art techniques, achieving \((6.8-8.7)\%\) and \((4.9-5.4)\%\) higher Accuracy-F1 scores, respectively.

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This study compares the proposed approach against SOTA models on two benchmark and publicly available datasets, with accurate citations provided.

Notes

  1. https://www.pewresearch.org/fact-tank/2021/01/12/more-than-eight-in-ten-americans-get-news-from-digital-devices/

  2. https://www.bbc.com/

  3. https://www.npr.org/

  4. https://www.naturalnews.com/

  5. https://humansbefree.com/

  6. https://www.factcheck.org/2020/02/fake-coronavirus-cures-part-1-mms-is-industrial-bleach/

  7. https://developer.twitter.com/en/docs/twitter-api/premium/search-api/overview

  8. https://developer.twitter.com/en/docs/twitter-api

  9. http://hyperopt.github.io/hyperopt/

  10. The best performance is achieved using italic (ReCOVery), underline (MMCoVaR) and bold (for both datasets) hyperparameter values.

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Acknowledgments

The First Author would like to thank the Faculty Initiation Grant (FIG), ABV-IIITM Gwalior, India to support this work.

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Author notes

  1. Saswata Roy and Manish Bhanu contributed equally to this work.

Authors and Affiliations

  1. Department of Information Technology, Atal Bihari Vajpayee Indian Institute of Information Technology and Management(ABV-IIITM), Gwalior, Morena Rd, Gwalior, 474015, Madhya Pradesh, India

    Saswata Roy

  2. Department of Computer Science and Engineering, Rajiv Gandhi Institute of Petroleum Technology, Jais, Amethi, 229304, Uttar Pradesh, India

    Manish Bhanu

  3. Department of Computer Science and Engineering, Indian Institute of Technology Roorkee, Haridwar Highway, Roorkee, 247667, Uttarakhand, India

    Shalini Priya

  4. Department of Computer Science and Engineering, Indian Institute of Technology Patna, Bihta, Patna, 801106, Bihar, India

    Joydeep Chandra & Sourav Kumar Dandapat

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  1. Saswata Roy
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  2. Manish Bhanu
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  3. Shalini Priya
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  4. Joydeep Chandra
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  5. Sourav Kumar Dandapat
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Contributions

Saswata Roy: Conceptualization, Methodology, Software, Writing - Original draft preparation. Manish Bhanu: Conceptualization, Methodology, Software, Writing - Original draft preparation. Shalini Priya: Supervision, Visualization, Formal analysis and investigation. Joydeep Chandra: Supervision, Visualization, Formal analysis and investigation. Sourav Kumar Dandapat: Supervision, Visualization, Formal analysis and investigation.

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Correspondence to Saswata Roy or Manish Bhanu.

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Our research utilized data from two publicly available multimodal repositories, with accurate citations provided.

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Roy, S., Bhanu, M., Priya, S. et al. Beyond just saying it’s false: explainable AI for multimodal misinformation detection. Appl Intell 55, 781 (2025). https://doi.org/10.1007/s10489-025-06656-2

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