Using Deep Learning to Detect Rumors in Twitter

被引：7

作者：

Providel, Eliana ^{[1
,2
]}

Mendoza, Marcelo ^{[1
]}

机构：

[1] Univ Tecn Federico Santa Maria, Dept Informat, Santiago, Chile

[2] Univ Valparaiso, Escuela Ingn Civil Informat, Valparaiso, Chile

来源：

SOCIAL COMPUTING AND SOCIAL MEDIA. DESIGN, ETHICS, USER BEHAVIOR, AND SOCIAL NETWORK ANALYSIS, SCSM 2020, PT I | 2020年 / 12194卷

关键词：

Rumor detection; Empirical factors; Deep learning;

D O I：

10.1007/978-3-030-49570-1_22

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

The automatic detection of rumors in social networks is an important problem that would allow counteracting the effects that the propagation of false information produces. We study the performance of deep learning architectures in this problem, analyzing ten different machines on word2vec and BERT. Our results show that some architectures are more suitable for some particular classes, suggesting that the use of committee machines would offer advantages in this task.

引用

页码：321 / 334

页数：14

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