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 -Gos-sipCop 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.
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 -Gos-sipCop 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.
The digital era has witnessed a significant rise in misinformation, underscoring the urgent need for effective tools to distinguish factual news from fabricated content.This study proposes a novel methodology for fake news detection that leverages quantum entanglement to facilitate multimodal fusion of textual, visual, and acoustic data.The quantum entanglement algorithm offers significant advantages in managing high-dimensional data by enabling efficient feature optimization through quantum computing encodings integrated with neural network architectures.The existing quantum circuits for text, visual, and audio would co-occur while witnessing the message from humans to machines with the machines acting as quantum computers integrated with neural networks, specifically designed for fake news detection.The results generated from our method demonstrate significantly improved fake news detection accuracy, and increased accuracy in the noise simulation, and the system is resilient to adversarial methods, all in contrast to typical methods.The proposed Quantum Encoding with Multimodal Fusion (QEMF) framework surpasses existing approaches by offering as it suggests promising future directions to tackle fake news across the web.The system, in a scientific manner, performs a variety of strict pre-processing techniques to all textual data such as tokenization, stemming, and lemmatization, along with sophisticated image pre-processing to all visual data.It uses the latest extracting features, i.e., Glove for text embedding, and conventional convolutional networks, like VGG16, for visual data.The feature representation is significantly enriched through quantum encoding, while the capability of QCNNs to identify the most salient and discriminative features enhances accuracy and ensures robustness against noise and adversarial interference.Furthermore, the system's real-time detection capability and its scalability position it as a powerful tool in combating misinformation within the evolving bio-informational ecosystem.
Today’s digital world facilitates the rapid growth of social media platforms and online news sources and consequently the spread of true and false information. While the detection of fake news is extensively studied using classical machine learning (ML)-based approaches, quantum ML approaches remain largely unexplored. In this study, we aim to investigate the effectiveness of a hybrid quantum-classical neural network on three open access datasets. This study proposes a Hybrid Quantum Neural Network (HQNN) model for classifying news articles as either "fake" or "real". The approach combines classical machine learning techniques with quantum computing to leverage the strengths of both paradigms. It incorporates a quantum layer, implemented via a parameterized quantum circuit that utilizes angle embedding and entanglement to extract complex features, followed by a classical fully connected layer for final decision-making. Our results demonstrate the effectiveness of the HQNN model, achieving high accuracy across multiple datasets, with values reaching up to 90.71%. These findings highlight the potential of hybrid quantum-classical models to improve fake news detection and pave the way for further research into quantum-enhanced ML applications.
Many prevalent issues in today’s society, such as fake news detection, can be efficiently addressed using artificial intelligence and machine learning techniques. The rapid dissemination of fake news through social media makes it challenging to verify the validity of information. QML and QDL represent promising frontiers for building models to address these challenges, with the prospect of achieving quantum advantage driving advancements in the field. This study examines and compares literature on QML and QDL models, including QSVM, VQE, QNN, QCNN, and RQNN, using the MNIST dataset. The effectiveness and scope of application of these models for fake news detection and other real-world applications are analyzed.
The rapid spread of fake news on social media poses a significant threat to modern societies. Traditional machine learning approaches have limitations in handling the ever-increasing volume and complexity of data. This research explores quantum machine learning for fake news classification by proposing Pegasos Quantum Support Vector Machines, a novel algorithm combining Pegasos Support Vector Machines with quantum kernels, and advanced data encoding. Through experimentation on the IBM Qasm Simulator, Pegasos Quantum Support Vector Machines scored 90.67% in accuracy. This study is primarily focused on local simulation, where the proposed algorithm scored as high as 95.63%, with 95.44% precision, 99.52% recall, and 96.76% f1-score. The achieved results outperform other machine learning methods on the BUZZFEED dataset, including Quantum Neural Networks and Quantum K-Nearest Neighbors. Its successful implementation paves the way for further refinement of quantum machine learning techniques in fake news classification. The PegasosQSVM algorithm encounters, however, some implementation issues on real world Quantum Processing Units(QPU). Noisy Intermediate-Scale Quantum era QPU are prone to noise effects that affect the computations negatively, and by extension, the results of quantum machine learning algorithms. Further implementation on real QPU and use of error mitigation techniques, are needed for optimal results on quantum hardware.
Advancements in artificial intelligence (AI) and the increasing use of large language models (LLMs) havesignificantly impacted social computing, where the ease of generating and distributing text has amplifiedthe spread of misinformation and fake news. The problem addressed in this paper is the challenge ofdetecting fake news in a contextual manner, especially in the context of the vast amounts of text datagenerated on social media and other platforms. To tackle this, we propose a novel solution combiningQuantum Transfer Learning using BERT (Bidirectional Encoder Representations from Transformers)with Variational Quantum Circuits (VQCs). Our approach leverages the power of quantum computingto enhance the accuracy and reliability of fake news detection models, introducing a hybrid quantumclassical framework to improve detection performance. Through experiments on the CONSTRAINTCOVID-19 and IFND Political datasets, we demonstrate that the proposed Quantum Deep Learning(QDL) model achieves competitive performance over several classical deep learning baselines in termsof detection accuracy and contextual understanding. This paper presents a new direction for leveragingquantum computing in social computing applications, helping to develop better ways to mitigate falseinformation.
The rise of deepfake technologies has posed significant challenges to privacy, security, and information integrity, particularly in audio and multimedia content. This paper introduces a Quantum-Trained Convolutional Neural Network (QT-CNN) framework designed to enhance the detection of deepfake audio, leveraging the computational power of quantum machine learning (QML). The QT-CNN employs a hybrid quantum-classical approach, integrating Quantum Neural Networks (QNNs) with classical neural architectures to optimize training efficiency while reducing the number of trainable parameters. Our method incorporates a novel quantum-to-classical parameter mapping that effectively utilizes quantum states to enhance the expressive power of the model, achieving up to 70% parameter reduction compared to classical models without compromising accuracy. Data pre-processing involved extracting essential audio features, label encoding, feature scaling, and constructing sequential datasets for robust model evaluation. Experimental results demonstrate that the QT-CNN achieves comparable performance to traditional CNNs, maintaining high accuracy during training and testing phases across varying configurations of QNN blocks. The QT framework’s ability to reduce computational overhead while maintaining performance underscores its potential for real-world applications in deepfake detection and other resource-constrained scenarios. This work highlights the practical benefits of integrating quantum computing into artificial intelligence, offering a scalable and efficient approach to advancing deepfake detection technologies.