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为了实现虚假新闻的早期检测，提出一种基于预训练表示和宽度学习的虚假新闻早期检测方法。首先，将新闻文本输入大规模预训练语言模型RoBERTa中，得到对应新闻文本的上下文语义表示。其次，将得到的新闻文本的上下文语义表示输入宽度学习的特征节点和增强节点中，利用宽度学习的特征节点和增强节点进一步提取新闻文本的线性和非线性特征并构造分类器，从而预测新闻的真实性。最后，在3个真实数据集上进行了对比实验，结果表明，所提方法可以在4 h内检测出虚假新闻，准确率超过80%,优于基线方法。
Abstract：In order to achieve early detection of fake news, a method based on pre-training representation and broad learning was proposed. Firstly, the news text was input into the RoBERTa large-scale pre-training language model to obtain the contextual semantic representation of the corresponding news text. Secondly, the obtained contextual semantic representation was fed into the feature nodes and enhanced nodes of broad learning. By leveraging these broad learning nodes, both linear and non-linear features were extracted from the news text, enabling the construction of a classifier for predicting the authenticity of the news. Finally, comparative experiments were conducted on three real datasets, and the results demonstrated that the proposed method was capable of detecting fake news within 4 h with an accuracy rate exceeding 80%, surpassing the performance of the baseline method.
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基本信息:
DOI：10.13705/j.issn.1671-6841.2023129
中图分类号:G210.7;TP391.1
引用信息:
[1]胡舜邦,王琳,刘伍颖.基于预训练表示和宽度学习的虚假新闻早期检测[J].郑州大学学报(理学版),2025,57(02):31-36.DOI:10.13705/j.issn.1671-6841.2023129.
基金信息:
教育部人文社会科学研究规划基金项目(20YJAZH069);教育部人文社会科学研究青年基金项目(20YJC740062); 山东省研究生教育教学改革研究项目(SDYJG21185); 山东省本科教学改革研究重点项目(Z2021323); 上海市哲学社会科学“十三五”规划课题(2019BYY028)