Automatic Fake News Detection based on Deep Learning, FastText and News Title
نویسندگان
چکیده
As a range of daily phenomena, Fake News is quickly becoming longstanding issue affecting individuals, public and private sectors. This major challenge the connected modern world can cause many severe real damages such as manipulating opinion, damaging reputations, contributing to loss in stock market value representing risks global health. With fast spreading online misinformation, checking manually becomes ineffective solution (not obvious, difficult takes long time). The improvement Deep Learning Networks (DLN) support with high degree accuracy efficiency classical processes spotting. One keys strategies are optimizing Word Embedding Layer (WEL) finding relevant predicting features. In this context, based on six DLN architectures, FastText process WEL Inverted Pyramid Articles Pattern (IPP), present paper focuses assessment first news article feature that hypothesized performances fake predicting: Title. By assessing impact Vector Size (EVS), Window (WS) Minimum Frequency Words (MFW) Titles corpus have DLN, experiments carried out showed Title significant detection rates exceeding 98%.
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ژورنال
عنوان ژورنال: International Journal of Advanced Computer Science and Applications
سال: 2022
ISSN: ['2158-107X', '2156-5570']
DOI: https://doi.org/10.14569/ijacsa.2022.0130118