计算机科学 ›› 2022, Vol. 49 ›› Issue (8): 330-335.doi: 10.11896/jsjkx.210600046

• 信息安全 • 上一篇    下一篇

基于改进位置编码的谣言检测模型

姜梦函, 李邵梅, 郑洪浩, 张建朋   

  1. 中国人民解放军战略支援部队信息工程大学信息技术研究所 郑州 450000
  • 收稿日期:2021-06-07 修回日期:2021-10-20 发布日期:2022-08-02
  • 通讯作者: 李邵梅(m19139795259@163.com)
  • 作者简介:(13513127249@163.com)
  • 基金资助:
    国家自然基金青年科学基金(62002384);郑州市协同创新重大专项(162/32410218);中国博士后科学基金面上项目(47698)

Rumor Detection Model Based on Improved Position Embedding

JIANG Meng-han, LI Shao-mei, ZHENG Hong-hao, ZHANG Jian-peng   

  1. Institute of Information Technology,PLA Strategic Support Force Information Engineering University, Zhengzhou 450000,China
  • Received:2021-06-07 Revised:2021-10-20 Published:2022-08-02
  • About author:JIANG Meng-han,born in 1996,postgraduate.Her main research interests include natural language processing and so on.
    LI Shao-mei,born in 1982,Ph.D,asso-ciate professor.Her main research in-terests include natural language processing and so on.
  • Supported by:
    Young Scientists Funds of the National Natural Science Foundation of China(62002384),Zhengzhou Collaborative Innovation Major Project(162/32410218) and China Postdoctoral Science Foundation(47698).

摘要: 随着在线社交网络的兴起,人们传播和获取信息的方式发生了翻天覆地的变化。社交媒体在方便人们生活的同时,也加速了谣言的产生和传播。因此,如何准确高效地检测谣言成为了亟待解决的问题。为了提高谣言检测的精度,对基于全局-局部注意网络的谣言检测模型进行了改进,考虑到文本中词与词之间的位置关系对谣言检测的影响,引入了一种新的相对位置编码方法来改进原有模型的局部特征提取模块。该方法能够更准确地提取谣言中文本的语义信息和位置信息并将它们聚合,得到更优的区分谣言与非谣言的文本特征,将该特征和描述转发行为的全局特征相结合,进而提升对谣言的检测效果。实验结果表明,与其他主流检测方法相比,所提方法在微博数据集上的F1值可达95.0%,具有更好的检测效果。

关键词: 深度学习, 相对位置编码, 谣言检测, 谣言文本特征, 注意力机制

Abstract: With the rise of online social networks,the way people disseminate and obtain information has changed drastically.While social media facilitates people’s lives,it also accelerates the generation and spread of rumors.For this reason,detect rumors accurately and efficiently becomes an urgent problem to be solved.In order to improve the accuracy of rumor detection,the rumor detection model based on the global-local attention network has been improved.Taking into account the influence of the positional relationship between words in the text on rumor detection,a new relative position encoding method is introduced to improve the local feature extraction module of the original model.This method can more accurately extract and aggregate the semantic information and location information of the text in the rumor,and obtain better text features that distinguish between rumors and non-rumors.The combination of features and global features describing forwarding behavior improves the detection effect of rumors.Experimental results show that,compared with other mainstream detection methods,the F1 value of the proposed method can reach 95.0% on the Microblog data set,which has a better detection effect.

Key words: Attention mechanism, Deep learning, Relative position embedding, Rumor detection, Rumor text features

中图分类号: 

  • TP391
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