Quantum Transfer Learning for Social Computing Applications: A Case Study With Fake News Detection

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.