Abstract: The widespread popularity of the Internet and intelligent devices has provided convenience for the public to access news. However, this also creates a breeding ground for the generation and propagation of fake news. Fake news spans multiple domains, whereas existing detection models often overlook the specificity of corpora across different domains, limiting their accuracy. To adress this issue, this paper proposes a multidomain fake news detection model based on prompt learning and fuzzy labels. This model employs RoBERTa to extract textual features and reformulates the detection task as a cloze problem by constructing prompt templates containing domain characteristics; meanwhile, it utilizes domain fuzzy membership probabilities generated by a neural network to guide the prompt learning process, effectively enhancing accuracy and generalization ability. Experimental results on the public datasets Weibo17 and Weibo21 demonstrate that this model outperforms traditional finetuning methods and existing stateoftheart methods under both domainunlabeled and multidomain conditions, with an average F1 score improvement of 1.16 percentage points, validating its feasibility and effectiveness in multidomain fake news detection tasks.

Key words: fake news detection; prompt learning; fuzzy set; mutildomain learning; deep learning

摘要： 互联网和智能设备的广泛普及为公众获取新闻提供了便利性.然而也为虚假新闻的产生和传播提供了温床.虚假新闻跨越多个领域，而现有的检测模型通常忽略领域间语料的特异性，影响了模型的准确率.为此，提出了一种基于提示学习和模糊标签的多领域虚假新闻检测模型(PLFuFND).该模型利用RoBERTa提取文本特征，通过构造含领域特性的提示模板将检测任务转化为填空问题；同时利用神经网络生成的领域模糊隶属概率指导提示学习，有效提升了准确率和泛化能力.在公开数据集Weibo17,Weibo21上的实验结果表明，该模型在无领域标注和多领域条件下的表现均优于传统微调模型及现有的先进模型，F1分数平均提升了1.16个百分点，验证了其在多领域虚假新闻检测任务中的可行性和有效性.

关键词: 虚假新闻检测；提示学习；模糊集合；多领域学习；深度学习