Lu Ying, Yao Naiwei
Xi'an International University, Xi'an City, 710000, China.
Northwest Electric Power Design Institute Co., Ltd., Xi'an City, 710000, China.
Sci Rep. 2025 Jul 1;15(1):20544. doi: 10.1038/s41598-025-05702-w.
To improve the accuracy and efficiency of fake news detection, this study proposes a deep learning model that integrates residual networks with attention mechanisms. Building on traditional convolutional neural networks, the model incorporates multi-head attention mechanisms to enhance the extraction of key features from multimodal data such as text, images, and videos. Additionally, residual connections are introduced to deepen the network architecture, mitigate the vanishing gradient problem, and improve the model's learning depth and stability. Compared with existing approaches, this study introduces several key innovations. First, it constructs a multimodal feature fusion module that integrates text, image, and video data. Second, it designs a cross-modal alignment mechanism to better connect information across different data types. Third, it optimizes the feature fusion structure for more effective integration. Finally, the study employs attention mechanisms to highlight and enhance the representation of salient features. Experiments were conducted using three representative datasets: the LIAR dataset for political short texts, the FakeNewsNet dataset for English multimodal news, and the Weibo dataset from a Chinese social media platform. These were selected to comprehensively evaluate the model's performance across different scenarios. Baseline models used for comparison include Bidirectional Encoder Representations from Transformers (BERT), Robustly Optimized Bidirectional Encoder Representations from Transformers Approach (RoBERTa), Generalized Autoregressive Pretraining for Language Understanding (XLNet), Enhanced Representation through Knowledge Integration (ERNIE), and Generative Pre-trained Transformer 3.5 (GPT-3.5). In terms of four key performance metrics-accuracy, precision, recall, and F1 score-the proposed model achieved best-case values of 0.977, 0.986, 0.969, and 0.924, respectively, outperforming the aforementioned baseline models overall. Furthermore, simulated experiments were conducted to evaluate the model's real-world applicability from four dimensions: robustness, generalization ability, response time, and resource consumption. The results demonstrate that the model maintains strong stability and adaptability under data perturbations and diverse input conditions, with a response time controllable within 0.02 s. The model also shows significant computational advantages when handling large-scale datasets. Therefore, this study presents a high-performance and deployment-friendly solution for fake news detection in multimodal contexts. The study also offers valuable theoretical insights and practical guidance for applying deep learning to public opinion governance and text classification.
为提高虚假新闻检测的准确性和效率，本研究提出一种将残差网络与注意力机制相结合的深度学习模型。该模型基于传统卷积神经网络构建，融入多头注意力机制以增强从文本、图像和视频等多模态数据中提取关键特征的能力。此外，引入残差连接以加深网络架构，缓解梯度消失问题，并提高模型的学习深度和稳定性。与现有方法相比，本研究引入了几个关键创新点。首先，构建了一个整合文本、图像和视频数据的多模态特征融合模块。其次，设计了一种跨模态对齐机制，以更好地连接不同数据类型的信息。第三，优化了特征融合结构以实现更有效的整合。最后，本研究采用注意力机制来突出和增强显著特征的表示。使用三个具有代表性的数据集进行了实验：用于政治短文本的LIAR数据集、用于英语多模态新闻的FakeNewsNet数据集以及来自中国社交媒体平台的微博数据集。选择这些数据集是为了全面评估模型在不同场景下的性能。用于比较的基线模型包括来自Transformer的双向编码器表示（BERT）、来自Transformer方法的稳健优化双向编码器表示（RoBERTa）、用于语言理解的广义自回归预训练（XLNet）、通过知识整合增强表示（ERNIE）以及生成式预训练Transformer 3.5（GPT - 3.5）。在准确性、精确率、召回率和F1分数这四个关键性能指标方面，所提出的模型分别取得了0.977、0.986、0.969和0.924的最佳值，总体上优于上述基线模型。此外，进行了模拟实验，从鲁棒性、泛化能力、响应时间和资源消耗四个维度评估模型的实际适用性。结果表明，该模型在数据扰动和多样的输入条件下保持了强大的稳定性和适应性，响应时间可控制在0.02秒以内。该模型在处理大规模数据集时也显示出显著的计算优势。因此，本研究为多模态环境下的虚假新闻检测提供了一种高性能且便于部署的解决方案。该研究还为将深度学习应用于舆论治理和文本分类提供了有价值的理论见解和实践指导。
