Khalid Madiha, Mushtaq Muhammad Faheem, Akram Urooj, Safran Mejdl, Alfarhood Sultan, Ashraf Imran
Faculty of Computing, The Islamia University of Bahawalpur, Bahawalpur, 63100, Pakistan.
Research Chair of Online Dialogue and Cultural Communication, Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh, 11543, Saudi Arabia.
Sci Rep. 2025 Aug 3;15(1):28305. doi: 10.1038/s41598-025-10661-3.
With the growth of social media, people are sharing more content than ever, including X posts that reflect a variety of emotions and opinions. AI-generated synthetic text, known as deepfake text, is used to imitate human writing to disseminate misleading information and fake news. However, as deepfake technology continues to grow, it becomes harder to accurately understand people's opinions on deepfake posts. Existing sentiment analysis algorithms frequently fail to capture the domain-specific, misleading, and context-sensitive characteristics of deepfake-related content. This study proposes a hybrid deep learning (DL) approach and novel transfer learning (TL)-based feature extraction approach for deepfake posts' sentiment analysis. The transfer learning-based approach combines the strengths of the hybrid DL technique to capture global and local contextual information. In this study, we compare the proposed approach with a range of machine learning algorithms, as well as, DL techniques for validation. Different feature extraction techniques, such as a bag of words (BOW), term frequency-inverse document frequency (TF-IDF), word embedding features, and novel TL features that combine the LSTM and DT, are used to build the models. The ML models are fine-tuned with extensive hyperparameter tuning to enhance performance and efficiency. The sentiment analysis performance of each applied method is validated using the k-fold cross-validation. The experimental results indicate that the proposed LGR (LSTM+GRU+RNN) approach with novel TL features performs well with a 99% accuracy. The proposed approach helps detect and prevent the spread of deepfake content, keeping people and organizations safe from its negative effects. This study covers a crucial gap in evaluating deepfake-specific social media sentiment by providing a comprehensive, scalable mechanism for monitoring and reducing the effect of fake content online.
随着社交媒体的发展，人们分享的内容比以往任何时候都多，包括反映各种情感和观点的X帖子。人工智能生成的合成文本，即深度伪造文本，被用于模仿人类写作以传播误导性信息和假新闻。然而，随着深度伪造技术的不断发展，准确理解人们对深度伪造帖子的看法变得越来越困难。现有的情感分析算法常常无法捕捉与深度伪造相关内容的特定领域、误导性和上下文敏感特征。本研究提出了一种混合深度学习（DL）方法和基于新颖迁移学习（TL）的特征提取方法，用于深度伪造帖子的情感分析。基于迁移学习的方法结合了混合DL技术的优势，以捕捉全局和局部上下文信息。在本研究中，我们将所提出的方法与一系列机器学习算法以及DL技术进行比较以进行验证。使用不同的特征提取技术，如词袋（BOW）、词频-逆文档频率（TF-IDF）、词嵌入特征以及结合了长短期记忆网络（LSTM）和决策树（DT）的新颖TL特征来构建模型。通过广泛的超参数调整对机器学习模型进行微调，以提高性能和效率。使用k折交叉验证来验证每种应用方法的情感分析性能。实验结果表明，所提出的具有新颖TL特征的LGR（LSTM+GRU+RNN）方法表现良好，准确率达到99%。所提出的方法有助于检测和防止深度伪造内容的传播，使个人和组织免受其负面影响。本研究通过提供一种全面、可扩展的机制来监测和减少在线虚假内容的影响，填补了评估深度伪造特定社交媒体情感方面的关键空白。
