Early Rumor Detection based on Data Augmentation and Pre-training Transformer

被引：2

作者：

Hu, Yanjun ^{[1
]}

Ju, Xinyi ^{[1
]}

Ye, Zhousheng ^{[1
]}

Khan, Sulaiman ^{[1
]}

Yuan, Chengwu ^{[1
]}

Lai, Qiran ^{[1
]}

Liu, Junqiang ^{[1
]}

机构：

[1] Zhejiang Gongshang Univ, Sch Informat & Elect Engn, Hangzhou, Peoples R China

来源：

2022 IEEE 12TH ANNUAL COMPUTING AND COMMUNICATION WORKSHOP AND CONFERENCE (CCWC) | 2022年

基金：

中国国家自然科学基金;

关键词：

Transformer; data augmentation; rumor detect; deep learning;

D O I：

10.1109/CCWC54503.2022.9720776

中图分类号：

TP31 [计算机软件];

学科分类号：

081202 ; 0835 ;

摘要：

The development of online social networks such as Sina Weibo, Twitter and Facebook has completely changed the way people communicate. While online social media makes it easier for people to get information, it also results in proliferation and wide spread of rumors. Automatic detection of rumors has become a hot topic of research. However, detecting rumors in the early stage is quite challenging. This paper proposes a new model (DAPT) for early rumor detection by analyzing text features, using pre-training technique, and employing data augmentation method. Experiments on BuzzFeed, PolitiFact, and Twitter16 datasets show that the accuracy and F1-score by the proposed model are better than the existing models.

引用

页码：152 / 158

页数：7

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