融合语义增强的社交媒体虚假信息检测方法研究<sup>*</sup>

doi:10.11925/infotech.2096-3467.2022.0923

数据分析与知识发现

2023, Vol. 7

Issue (2): 48-60 https://doi.org/10.11925/infotech.2096-3467.2022.0923

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融合语义增强的社交媒体虚假信息检测方法研究^*

王昊^1,²,龚丽娟^1,²,周泽聿^1,²(

),范涛^1,²,王永生^1,²

¹南京大学信息管理学院南京 210023
²江苏省数据工程与知识服务重点实验室南京 210023

Detecting Mis/Dis-information from Social Media with Semantic Enhancement

Wang Hao^1,²,Gong Lijuan^1,²,Zhou Zeyu^1,²(

),Fan Tao^1,²,Wang Yongsheng^1,²

¹School of Information Management, Nanjing University, Nanjing 210023, China
²Jiangsu Key Laboratory of Data Engineering and Knowledge Service, Nanjing 210233, China

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摘要

【目的】通过构建自动化检测模型有效识别社交媒体中的虚假信息，探讨如何解决人工识别、单特征机器学习等现存方法难以兼顾海量数据处理的速度与准确性的问题。【方法】本文以新浪微博社交平台为研究对象，以单一文本特征BFID模型作为实验基准模型，提出两种融合语义增强的虚假信息检测方法。【结果】以单一文本特征BFID模型的结果为基线，本文提出的融合情感特征的BFID-SEN模型在虚假信息识别的部分准确率上提升约1.59个百分点；融合图片特征的BFID-IMG模型通过结合深度残差网络ResNet，在虚假信息识别的部分准确率上稳定提升约0.78个百分点。【局限】由于融合情感特征的语料数量、情感类别与多模态虚假信息数据集有限，模型训练不充分，因此语义增强的融合效果有限。【结论】本文提出的两种融合语义增强方法均能在一定程度上更好地识别虚假信息。

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	王昊
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关键词 ：虚假信息, 语义增强, 多模态, 新浪微博, 情感分析

Abstract：

[Objective] This paper builds an automated detection model to effectively identify mis/dis-information from social media, aiming to balance the speed and accuracy of processing massive data. [Methods] The classification model is the mainstream processing technique to detect for mis/dis-information. However, most of them could not extract deep semantic features from the texts. Therefore, we used the single text feature BFID model (BERT False-Information-Detection) as the benchmark model, and proposed two new methods with fused semantic enhancement to detect the mis/dis-information. [Results] We examined the new models with data from Sina Weibo. The accuracy of the model based on fused sentiment feature BFID-SEN (BFID-Sentiment) increased about 1.59 percentage point, while the accuracy of model with fused image feature BFID-IMG (BFID-Image) model improved by 0.78 percentage point. [Limitations] The ability to fuse semantic enhancement is limited due to the small corpus size, sentiment categories and multimodal disinformation training datasets. [Conclusions] The proposed methods are able to more effectively identify false information from social media.

Key words： False Information Semantic Enhancement Multi-Modal Sina Weibo Sentiment Analysis

收稿日期: 2022-08-31 出版日期: 2022-11-09

ZTFLH:

TP391

基金资助:^*国家自然科学基金项目(72074108);中央高校基本科研业务费专项资金资助项目的研究成果之一(010814370113)

通讯作者: 周泽聿，ORCID：0000-0003-2757-2992，E-mail： mf20140111@smail.nju.edu.cn。

引用本文:

王昊, 龚丽娟, 周泽聿, 范涛, 王永生. 融合语义增强的社交媒体虚假信息检测方法研究^*[J]. 数据分析与知识发现, 2023, 7(2): 48-60.
Wang Hao, Gong Lijuan, Zhou Zeyu, Fan Tao, Wang Yongsheng. Detecting Mis/Dis-information from Social Media with Semantic Enhancement. Data Analysis and Knowledge Discovery, 2023, 7(2): 48-60.

链接本文:

https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.2096-3467.2022.0923 或 https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/Y2023/V7/I2/48

Fig.1 研究框架

Layer Name	Output Size	18-Layer	34-Layer	50-Layer	101-Layer
Conv1	$112 × 112$	$7 × 7, ? 64, ? s t r i d e ? 2$
Conv2_x	$56 × 56$	$3 × 3 m a x ? p o o l, ? s t r i d e ? 2$
Conv2_x	$56 × 56$	$3 × 3, ? 64 3 × 3, ? 64 × 2$	$3 × 3, ? 64 3 × 3, ? 64 × 3$	$1 × 1, ? 64 3 × 3, ? 64 1 × 1,256 × 3$	$1 × 1, ? 64 3 × 3, ? 64 1 × 1,256 × 3$
Conv3_x	$28 × 28$	$3 × 3, ? 128 3 × 3,128 × 2$	$3 × 3, ? 128 3 × 3, ? 128 × 4$	$1 × 1,128 3 × 3,128 1 × 1,512 × 4$	$1 × 1,128 3 × 3, ? 128 1 × 1,512 × 4$
Conv4_x	$14 × 14$	$3 × 3,256 3 × 3,256 × 2$	$3 × 3, ? 256 3 × 3, ? 256 × 6$	$1 × 1, ? 256 3 × 3, ? 256 1 × 1,1024 × 6$	$1 × 1, ? 256 3 × 3, ? 256 1 × 1,1024 × 23$
Conv5_x	$7 × 7$	$3 × 3, ? 512 3 × 3, ? 512 × 2$	$3 × 3, ? 512 3 × 3,512 × 3$	$1 × 1, ? 512 3 × 3, ? 512 1 × 1,2048 × 3$	$1 × 1, ? 512 3 × 3, ? 512 1 × 1,2048 × 3$
	$1 × 1$	Average pool， 1000-d fc， softmax
FLOPs		$1.8 × 109$	$3.6 × 109$	$3.8 × 109$	$7.6 × 109$

Table 1 不同ResNet模型各层结构

Table 2 去重后数据情况

Table 3 仅含文本数据集划分结果

Table 4 多模态虚假信息数据集划分结果

Fig.2 以BERT为基础的不同模型实验效果

Table 5 情感分析语料的分类结果

Fig.3 添加情感特征后不同模型的实验结果

Table 6 BFID-SEN模型与BFID模型结果对比

Fig.4 添加图片特征后不同模型的实验结果

Table 7 BFID-IMG模型与BFID模型结果对比

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