The spread of fake news on the virtual platform is so high that it requires efficient and scalable fake news detection systems. Recent language models based on transformers including DistilBERT have been shown to be very effective at extracting contextual semantic representation of text, but cannot express high-dimensional embedding space due to their dependency on classical classifiers. In the given work, we suggest QUATUM, a quantum-classical architecture with the incorporation of DistilBERT embeddings and variational quantum circuits to detect fake news. The suggested algorithm maps the output of transformers into a quantum Hilbert space to execute enhanced feature representation with superposition and entanglement on parameterized quantum circuits. They compare a traditional MLP-based classifier with the suggested quantum-enhanced model with the same training strategies on the IFND data set. It is shown that the QUATUM model has better performance, with the accuracy of 95.55%, and the equivalent values of the precision (96.45), recall (94.65), and F1-score (95.54). The results of these studies indicate that quantum-enhanced learning is effective to learn the complex decision boundaries and propose the possibility of using hybrid quantum-classical models to provide the next-generation fake news detection systems.
The spread of fake news on the virtual platform is so high that it requires efficient and scalable fake news detection systems. Recent language models based on transformers including DistilBERT have been shown to be very effective at extracting contextual semantic representation of text, but cannot express high-dimensional embedding space due to their dependency on classical classifiers. In the given work, we suggest QUATUM, a quantum-classical architecture with the incorporation of DistilBERT embeddings and variational quantum circuits to detect fake news. The suggested algorithm maps the output of transformers into a quantum Hilbert space to execute enhanced feature representation with superposition and entanglement on parameterized quantum circuits. They compare a traditional MLP-based classifier with the suggested quantum-enhanced model with the same training strategies on the IFND data set. It is shown that the QUATUM model has better performance, with the accuracy of 95.55%, and the equivalent values of the precision (96.45), recall (94.65), and F1-score (95.54). The results of these studies indicate that quantum-enhanced learning is effective to learn the complex decision boundaries and propose the possibility of using hybrid quantum-classical models to provide the next-generation fake news detection systems.
The advent of quantum computing has introduced new opportunities for enhancing classical machine learning architectures. In this study, we propose a novel hybrid model, the HQDNN (Hybrid Quantum–Deep Neural Network), designed for the automatic detection of fake news. The model integrates classical fully connected neural layers with a parameterized quantum circuit, enabling the processing of textual data within both classical and quantum computational domains. To assess its effectiveness, we conducted experiments on the widely used LIAR dataset utilizing Term Frequency–Inverse Document Frequency (TF-IDF) features, as well as transformer-based DistilBERT embeddings. The experimental results demonstrate that the HQDNN achieves a superior recall performance—92.58% with TF-IDF and 94.40% with DistilBERT—surpassing traditional machine learning models such as Logistic Regression, Linear SVM, and Multilayer Perceptron. Additionally, we compare the HQDNN with SetFit, a recent CPU-efficient few-shot transformer model, and show that while SetFit achieves higher precision, the HQDNN significantly outperforms it in recall. Furthermore, an ablation experiment confirms the critical contribution of the quantum component, revealing a substantial drop in performance when the quantum layer is removed. These findings highlight the potential of hybrid quantum–classical models as effective and compact alternatives for high-sensitivity classification tasks, particularly in domains such as fake news detection.
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.
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 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.
This study proposes a hybrid quantum-classical framework for depression detection from social media text, integrating a frozen DistilBERT encoder with a variational quantum circuit (VQC)-based classification layer. The motivation stems from challenges in clinical NLP, including overfitting on limited datasets and high parameter overhead in conventional deep learning classifiers. Experiments are conducted on a balanced subset of the Reddit Self-Reported Depression Diagnosis (RSDD) dataset comprising 6,000 users. The proposed model is evaluated against classical baselines, including TF-IDF with logistic regression and a fine-tuned DistilBERT model. Results indicate that the hybrid approach achieves competitive performance, with an F1-score of 0.925 (±0.009) and improved recall (0.942 ± 0.015) compared to the classical DistilBERT baseline. Additionally, the quantum classification layer requires significantly fewer trainable parameters (72) compared to the classical dense head, demonstrating improved parameter efficiency at the classification stage. While the results suggest that variational quantum circuits can serve as an alternative non-linear classifier in low-data settings, the findings are based on simulation and require further validation on real quantum hardware. This work contributes to the emerging area of quantum natural language processing by providing an empirical evaluation of hybrid architectures on a real-world clinical text dataset.
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.