2025 02 v.57 31-36
基于预训练表示和宽度学习的虚假新闻早期检测
基金项目(Foundation):
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)
邮箱(Email):
wyliu@ldu.edu.cn;
DOI:
10.13705/j.issn.1671-6841.2023129
中文作者单位:
广东外语外贸大学信息科学与技术学院;上海外国语大学贤达经济人文学院;鲁东大学山东省语言资源开发与应用重点实验室;广东外语外贸大学外国语言学及应用语言学研究中心;
摘要(Abstract):
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。
关键词(KeyWords):
早期检测;虚假新闻;预训练表示;宽度学习;文本分类
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参考文献
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[3] KWON S,CHA M,JUNG K,et al.Prominent features of rumor propagation in online social media[C]//Proceedings of the IEEE 13th International Conference on Data Mining.Piscataway:IEEE Press,2014:1103-1108.
[4] FENG S,BANERJEE R,CHOI Y.Syntactic stylometry for deception detection[C]//Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics.Stroudsburg:Association for Computational Linguistics,2012:171-175.
[5] MA J,GAO W,MITRA P,et al.Detecting rumors from microblogs with recurrent neural networks[C]//International Joint Conference on Artificial Intelligence.Amsterdam:Elsevier Press,2016:56-66.
[6] MA J,GAO W,WONG K F.Detect rumor and stance jointly by neural multi-task learning[C]//Proceedings of the Web Conference.New York:ACM Press,2018:585-593.
[7] BIAN T A,XIAO X,XU T Y,et al.Rumor detection on social media with bi-directional graph convolutional networks[C]//Proceedings of the AAAI Conference on Artificial Intelligence.Palo Alto:AAAI Press,2020:549-556.
[8] DEVLIN J,CHANG M W,LEE K,et al.Bert:pre-training of deep bidirectional transformers for language understanding[C]//Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics:Human Language Technologies.Stroudsburg:Association for Computational Linguistics,2019:4171-4186.
[9] VASWANI A,SHAZEER N,PARMAR N,et al.Attention is all You need[C]//Proceedings of the 31st International Conference on Neural Information Processing Systems.New York:ACM Press,2017:6000-6010.
[10] PHILIP C C L,LIU Z L.Broad learning system:an effective and efficient incremental learning system without the need for deep architecture[J].IEEE transactions on neural networks and learning systems,2018,29(1):10-24.
[11] CHU Y H,LIN H F,YANG L A,et al.Hyperspectral image classification with discriminative manifold broad learning system[J].Neurocomputing,2021,442:236-248.
[12] JIN J W,LI Y T,YANG T J,et al.Discriminative group-sparsity constrained broad learning system for visual recognition[J].Information sciences,2021,576:800-818.
[13] MA J,GAO W,WONG K F.Detect rumors in microblog posts using propagation structure via kernel learning[C]//Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics.Stroudsburg:Association for Computational Linguistics,2017:708-717.
[14] LIU Y,WU Y F.Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks[C]//Proceedings of the AAAI Conference on Artificial Intelligence.Palo Alto:AAAI Press,2018:354-361.
[15] CUI Y M,CHE W X,LIU T,et al.Revisiting pre-trained models for Chinese natural language processing[C]//Findings of the Association for Computational Linguistics:EMNLP 2020.Stroudsburg:Association for Computational Linguistics,2020:657-668.
[16] ZHAO Z,RESNICK P,MEI Q Z.Enquiring minds:early detection of rumors in social media from enquiry posts[C]//Proceedings of the 24th International Conference on World Wide Web.New York:ACM Press,2015:1395-1405.
[17] KWON S,CHA M,JUNG K.Rumor detection over varying time windows[J].PLoS one,2017,12(1):e0168344.
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)
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