@inproceedings{suneesh-palani-2025-qcnn,
    title = "{QCNN}-{MFND}: A Novel Quantum {CNN} Framework for Multimodal Fake News Detection in Social Media",
    author = "Suneesh, Arya  and
      Palani, Balasubramanian",
    editor = "Pal, Santanu  and
      Pakray, Partha  and
      Jain, Priyanka  and
      Ekbal, Asif  and
      Bandyopadhyay, Sivaji",
    booktitle = "Proceedings of the QuantumNLP{\{}:{\}} Integrating Quantum Computing with Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Mumbai, India (Hybrid)",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-ijcnlp-aacl/2025.quantumnlp-1.7/",
    pages = "44--52",
    ISBN = "979-8-89176-306-7",
    abstract = "Fake news on social media platforms poses significant threats to public trust and information integrity. This research explores the application of quantum machine learning (QML) techniques for detecting fake news by leveraging quantum computing{'}s unique capabilities. Our work introduces a hybrid quantum-classical framework that utilizes quantum convolutional neural networks (QCNNs) with angle and amplitude encoding schemes for processing multimodal features from text and images. Experiments conducted on benchmark datasets - GossipCop and Politifact - demonstrate that our quantum-enhanced model achieves superior performance compared to classical approaches, with accuracy rates of 88.52{\%} and 85.58{\%}, and F1 scores of 93.19{\%} and 90.20{\%} respectively. Our findings establish QML as a viable approach for addressing the challenges of fake news detection in the digital era."
}