Multimodal, Semi-supervised and Unsupervised web content credibility analysis Frameworks

被引：0
|
作者：
Saini, Naman [1 ]
Singhal, Mukul [1 ]
Tanwar, Mukul [1 ]
Meel, Priyanka [1 ]
机构：
[1] Delhi Technol Univ, Dept Informat Technol, New Delhi, India
来源：
PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON INTELLIGENT COMPUTING AND CONTROL SYSTEMS (ICICCS 2020) | 2020年
关键词：
Multimodal; semi-supervised; unsupervised; fake news; internet; social media; credibility; survey;
D O I：
10.1109/iciccs48265.2020.9121005
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 Internet has evolved to become one of the main sources to access and consume news. The reason being the low cost and rapid transmission of news on it through various means. Nevertheless, such characteristics of the Internet also makes it a breeding ground for the spread of fake news. The outcomes of this are far-reaching, mounting negativity over individuals as well as society. Hence comes the requirement for fake news detection research effort which is constantly being carried out. "Fake News" refers to forged news. It is a lie made up out of nothing which deceives the reader appearing as real news. There is not much review work done in the field of fake news detection methods. In this paper, we provide a survey on types of fake news disseminated and the solutions proposed to deal with detecting it. We focus our survey on the latest research fields in Fake News Detection namely Multimodal Frameworks, Semi-Supervised Frameworks, and Unsupervised Frameworks. We also state the advantages and disadvantages of each of the work mentioned and finally, highlight challenges that still concern the field of fake news detection.
引用
收藏
页码：948 / 955
页数：8
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