面向虚假新闻检测的社交媒体多模态数据集构建

doi:10.11959/j.issn.2096-109x.2023060

Abstract

Abstract:

The advent of social media has brought about significant changes in people’s lives.While social media allows for easy access and sharing of news, it has also become a breeding ground for the dissemination of fake news, posing a serious threat to social security and stability.Consequently, researchers have shifted their focus towards fake news detection.Although several deep learning-based solutions have been proposed, these methods heavily rely on large amounts of supporting data.Currently, there is a scarcity of existing datasets, particularly in Chinese, and the collected news articles are often limited to the same category.To enhance the detection of fake news, a new multi-modal fake news dataset (MFND) was developed, which comprised Chinese and English news data from ten diverse categories: politics, economy, entertainment, sports, international affairs, technology, military, education, health, and social life.The word frequencies and categories of the proposed fake news dataset were analyzed and compared with existing fake news datasets in terms of number of news, news categories, modal information and news languages.The results of the comparison demonstrate that the MFND dataset excels in terms of category information and news languages.Moreover, training and validating existing typical fake news detection methods with MFND dataset, the experimental results show an improvement of approximately 10% in model performance compared to existing mainstream fake news datasets.

Key words: social media, fake news detection, multi-modal, multi-category, dataset

CLC Number:

TP393

Guopeng GAO, Yaodong FANG, Yanfang HAN, Zhenxing QIAN, Chuan QIN. Construction of multi-modal social media dataset for fake news detection[J]. Chinese Journal of Network and Information Security, 2023, 9(4): 144-154.

Figures/Tables 9

数据集	真实新闻	虚假新闻	模态信息	类别数量	社交上下文	语种
Weibo^[14]	4 749	4 779	文本+图片	-	包含	1
MediaEval^[23]	6 225	9 596	文本+图片	-	不包含	1
FacebookHoax^[24]	6 577	8 923	文本+图片	1	包含	1
TI-CNN^[25]	11 941	8 074	文本+图片	1	不包含	1
Fakeddit^[26]	$527 049$	$628 501$	文本+图片	-	包含	1
MM-COVID^[27]	7 192	3 981	文本+图片	1	包含	$6$
MFND	5 000	5 000	文本、图片、文本+图片	$10$	不包含	2

References 32

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语言	真实新闻	虚假新闻
中文	4 000	4 000
英文	1 000	1 000

检测方法	批量大小	优化器	词向量大小
tanh-RNN^[8]	50	自适应学习	100
TextCNN^[30]	50	随机梯度下降	768
BERT^[31]	256	自适应矩估计	768
EANN^[29]	100	自适应矩估计	32
SpokeFake^[32]	256	自适应矩估计	768

方法		MFND				Weibo_Wang数据集^[29]
方法		Accuracy	Precision	Recall	F1-score	Accuracy	Precision	Recall	F1-score
	tanh-RNN^[8]	0.917	0.963	0.853	0.905	0.575	0.523	0.415	0.463
单模态	TextCNN^[30]	0.931	0.924	0.934	0.933	0.875	0.812	0.921	0.865
	BERT^[31]	0.980	0.987	0.975	0.981	0.858	0.888	0.868	0.878
多模态	EANN^[29]	0.982	0.969	0.995	0.982	0.803	0.866	0.731	0.793
	SpokeFake^[32]	0.982	0.995	0.97	0.982	0.854	0.812	0.895	0.854

Construction of multi-modal social media dataset for fake news detection

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Figures/Tables 9

References 32

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数据集分类	数据集名称	年份	标签数量
	CREDBANK^[17]	2015	2
	BuzzFeedNews^[18]	2016	4
单模态	LIAR^[19]	2017	6
	NaistCovid^[20]	2020	2
	FakeCovid^[21]	2020	2
	CrossCOVID19^[22]	2021	2
	MediaEval^[23]	2016	2
	Weibo^[14]	2017	2
多模态	FacebookHoax^[24]	2017	2
	TI-CNN^[25]	2018	2
	Fakeddit^[26]	2020	2、3、6
	MM-COVID^[27]	2020	2

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