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数据分析与知识发现  2023, Vol. 7 Issue (11): 56-67     https://doi.org/10.11925/infotech.2096-3467.2022.1012
  研究论文 本期目录 | 过刊浏览 | 高级检索 |
基于数据增强和多任务学习的突发公共卫生事件谣言识别研究*
曾子明(),张瑜
武汉大学信息管理学院 武汉 430072
Rumor Detection of Public Health Emergencies Based on Data Augmentation and Multi-Task Learning
Zeng Ziming(),Zhang Yu
School of Information Management, Wuhan University, Wuhan 430072, China
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摘要 

目的】 通过引入多任务学习模型和数据增强方法,解决突发公共卫生事件情景下谣言识别任务数据不平衡且带标签数据量少的问题。【方法】 首先提取突发公共卫生事件谣言文本特征构建替换词表,基于扩展同义词表构建CEDA方法对不平衡的谣言数据集进行增强,然后构建多任务学习模型融合突发公共卫生事件情感分类和谣言识别任务的领域信息,基于Transformer获取共享特征,通过BiLSTM模型获取谣言识别任务的独有特征,提升突发公共卫生事件谣言识别任务准确性。【结果】 本文所提多任务学习模型的F1值达到0.972,比基于不平衡数据集的模型和单任务学习模型分别高出0.006和0.007,与DC-CNN模型相比F1值提升0.024。【局限】 多任务学习模型的辅助任务仅包括情感二分类任务,需要对负面情感进行更细粒度的分类。【结论】 基于领域数据增强和多任务学习的方法能够有效提高突发公共卫生事件谣言识别的分类效果。

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曾子明
张瑜
关键词 突发公共卫生事件谣言识别数据增强多任务学习共享Transformer    
Abstract

[Objective] This paper proposes a new model with data augmentation and multi-task learning, aiming to address the issue of unbalanced data and insufficient labeled data in rumor detection during public health emergencies. [Methods] Firstly, we extracted the text features of public health emergency rumors to construct a replacement word list. Then, we developed the CEDA method based on the extended synonym table to enhance the unbalanced rumor dataset. Third, we built a multi-task learning model to integrate the domain information of public health emergency sentiment classification and rumor detection. Fourth, we obtained the shared features with Transformer and retrieved the unique features of the rumor detection task using the BiLSTM model. Finally, it helped us improve the accuracy of the rumor detection. [Results] The F1 value of the proposed model was 0.972, which was 0.006 and 0.007 higher than the model based on the unbalanced dataset and the single-task learning model. Compared with the DC-CNN model, the F1 value increased by 0.024. [Limitations] The multi-task learning model only includes binary classification of sentiments, requiring more fine-grained negative sentiment classification. [Conclusions] The proposed method can effectively classify public health emergency rumors.

Key wordsPublic Health Emergencies    Rumor Detection    Data Augmentation    Multi-Task Learning    Shared Transformer
收稿日期: 2022-09-25      出版日期: 2023-03-22
ZTFLH:  TP393 G350  
基金资助:*国家社会科学基金项目的研究成果之一(21BTQ046)
通讯作者: 曾子明,ORCID:0000-0001-9847-0358,E-mail: zmzeng1977@aliyun.com。   
引用本文:   
曾子明, 张瑜. 基于数据增强和多任务学习的突发公共卫生事件谣言识别研究*[J]. 数据分析与知识发现, 2023, 7(11): 56-67.
Zeng Ziming, Zhang Yu. Rumor Detection of Public Health Emergencies Based on Data Augmentation and Multi-Task Learning. Data Analysis and Knowledge Discovery, 2023, 7(11): 56-67.
链接本文:  
https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.2096-3467.2022.1012      或      https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/Y2023/V7/I11/56
Fig.1  基于数据增强和多任务学习的突发公共卫生事件中谣言识别框架
主题 词1 词2 词3 词4 词5
新冠病毒 冠状病毒 新冠肺炎 新冠 Coronavirus SARS-CoV-2
流行病 疫情 疫区 感染 确诊 死亡病例
政策 隔离 封城 防控 群体免疫 健康码
人物组织 世界卫生组织 钟南山 张文宏 李文亮 福奇
医疗用品 试剂盒 核酸检测 疫苗 抗体 雷神山
Table 1  突发公共卫生事件谣言主题词表
疫情相关词词频数 正向情感词词频数 负向情感词词频数 程度副词词频数
词语 次数 词语 次数 词语 次数 词语 次数
疫情 119 需要 24 恐慌 5 一定 19
病毒 106 希望 23 掉以轻心 3 非常 15
感染 89 同意 10 3 15
新冠病毒 83 注意 9 憋气 3 完全 14
口罩 81 重视 8 原谅 3 7
…… …… …… …… …… …… …… ……
Table 2  突发公共卫生事件谣言词频表(部分)
词语 TF-IDF值 词语 TF-IDF值 词语 TF-IDF值
中国 0.019 武汉 0.014 感染 0.012
美国 0.019 视频 0.013 隔离 0.012
病毒 0.015 口罩 0.013 新型冠状病毒 0.012
正常化 0.014 开学 0.012 医院 0.011
疫情 0.014 没有 0.012 …… ……
Table 3  突发公共卫生事件谣言关键词表(部分)
领域词表 主题词表 情绪词表 程度词表
原词 替换 原词 替换 原词 替换 原词 替换
口罩 N95 新冠肺炎 新冠病毒
新型肺炎
恐慌 惊慌失措
惶恐
一定 必定
肯定
开学 上班
复工
疫情 病情
疾病
掉以轻心 草率
潦草
非常 特别
格外
干咳 咳嗽
咳痰
疫区 灾区
污染区
憋气 烦躁
郁闷
完全 全然
通通
确诊病例 新冠肺炎患者 感染 传染
沾染
原谅 宽容
谅解
不少 许多
众多
驱疫 防疫
抗疫
确诊 诊断 生气 暴怒 千万 万万
绝对
…… …… …… …… …… …… …… ……
Table 4  突发公共卫生事件谣言替换词表(部分)
操作 文本
原微博 易感染者可以在未与患者见面的情况下,因为吸入了悬浮在空气中的病毒感染新冠肺炎。
随机交换 易感染者可以在未与患者见面的情况下,因吸为入了悬浮在空气中的感毒病染新冠肺炎。
随机删除 易感染者可以在与患者见面情况,因为吸入悬浮在空气中的病毒感染新冠肺炎
随机插入 易见面感染者可以在未与患者见面的情况下来龙去脉,因为吸入了悬浮在空气中的病毒感染新冠肺炎。
同义词替换 易感染者同意在未与病人会面的情况下,因吸吮了漂流在氛围中的病毒感染新冠病毒。
Table 5  CEDA数据示例
实验环境 配置详情
GPU NVIDIA GeForce RTX 3090
CPU AMD EPYC 7601
显存 24 GB
内存 64GB
开发语言 Python 3.8
深度学习框架 PyTorch 1.10.0+CUDA 11.3
Table 6  实验环境
超参数设置 模型参数 参数值
训练参数设置 batch_size 64
epoch 5
learning_rate 0.000 1
Transformer 自注意力层数(N) 6
自注意力头数 8
dropout 0.1
BiLSTM 词嵌入维度 768
隐藏层节点数 768
层数 2
dropout 0.1
Table 7  深度学习算法参数设置
Fig.2  不同文本改变率对F1值的影响
编号 数据增强方法 Accuracy Precision Recall F1
1 - 0.955 0.952 0.980 0.966
2 简单复制 0.956 0.945 0.989 0.966
3 SimBert 0.957 0.955 0.981 0.967
4 随机交换(change_rate=0.1) 0.963 0.955 0.989 0.971
5 随机删除(change_rate=0.4) 0.964 0.957 0.989 0.972
6 随机插入(change_rate=0.1) 0.963 0.955 0.989 0.971
7 同义词替换(change_rate=0.3) 0.954 0.958 0.975 0.965
8 扩展同义词替换(change_rate=0.3) 0.963 0.955 0.989 0.971
9 EDA 0.963 0.952 0.992 0.971
10 CEDA 0.964 0.962 0.984 0.972
Table 8  数据增强对比实验结果
共享层数 不共享层数 Accuracy Precision Recall F1
0 6 0.947 0.932 0.990 0.959
1 5 0.949 0.941 0.982 0.961
2 4 0.952 0.955 0.974 0.964
3 3 0.952 0.968 0.961 0.964
4 2 0.949 0.971 0.955 0.962
5 1 0.949 0.963 0.962 0.962
6 0 0.955 0.952 0.980 0.966
Table 9  不同超参数设置实验结果(MTL)
编号 模型 Accuracy Precision Recall F1
1 TextCNN 0.893 0.893 0.893 0.893
2 DPCNN 0.900 0.869 0.768 0.816
3 BERT 0.846 0.853 0.914 0.881
4 BRET-Att-BiLSTM 0.886 0.841 0.991 0.908
5 BERT-RCNN 0.935 0.926 0.922 0.924
6 DC-CNN 0.948 0.947 0.949 0.948
7 Single-Task 0.947 0.932 0.990 0.959
8 CEDA-Single-Task 0.954 0.947 0.984 0.965
9 MTL 0.955 0.952 0.980 0.966
10 CEDA-MTL 0.964 0.962 0.984 0.972
Table 10  多任务学习对比实验结果
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