期刊名称：International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN：2158-107X
电子版ISSN：2156-5570
出版年度：2022
卷号：13
期号：1
DOI：10.14569/IJACSA.2022.0130118
语种：English
出版社：Science and Information Society (SAI)
摘要：As a range of daily phenomena, Fake News is quickly becoming a longstanding issue affecting individuals, public and private sectors. This major challenge of the connected and modern world can cause many severe and real damages such as manipulating public opinion, damaging reputations, contributing to the loss in stock market value and representing many risks to the global health. With the fast spreading of online misinformation, checking manually Fake News becomes ineffective solution (not obvious, difficult and takes a long time). The improvement of Deep Learning Networks (DLN) can support with high degree of accuracy and efficiency the classical processes of Fake News spotting. One of the keys improvement strategies are optimizing the Word Embedding Layer (WEL) and finding relevant Fake News predicting features. In this context, and based on six DLN architectures, FastText process as WEL and Inverted Pyramid as News Articles Pattern (IPP), the present paper focuses on the assessment of the first news article feature that is hypothesized as affecting the performances of fake news predicting: News Title. By assessing the impact that the Embedding Vector Size (EVS), Window Size (WS) and Minimum Frequency of Words (MFW) in News Titles corpus can have on DLN, the experiments carried out in this paper showed that the News Title feature and FastText process can have a significant improvement on DLN fake news detection with accuracy rates exceeding 98%.
关键词：Fake news; automatic detection; deep learning; FastText; news title
