Abstract
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%.
Highlights
Nowadays, fake news and sophisticated disinformation can have serious real world negative effects [1]
Compared to many recent relevant studies on automatic fake detection based on the Deep Learning Networks (DLN) architectures, the main objective of these experiments is to improve the performance of the execution time, loss and accuracy by testing a new embedding process (FastText), and reducing the dimensionality of the used articles news data by assessing the impact of the first news title feature that is hypothesized as affecting the performances of automatic Fake News spotting
This study investigates the impact of news articles titles on Fake News DLN spotting performances by using six DLN models: Simple Long Short Term Memory Networks (LSTM) (SI_LSTM), Stacked LSTM (ST_LSTM), Bidirectional LSTM (BI_LSTM), Simple Gated Recurrent Unit Network (GRU) (SI_GRU), Stacked GRU (ST_GRU) and Bidirectional GRU (BI_GRU)
Summary
Fake news and sophisticated disinformation can have serious real world negative effects [1]. One of the key improvement strategies is optimizing the main Fake News predicting features and Word Embedding Layer (WEL) In this context, and based on many DLN architectures and the news articles pattern IPP (Inverted Pyramid Pattern), the present paper focuses on the assessment of the first news feature that is hypothesized as affecting the performances of automatic Fake News spotting: News articles title. Compared to many recent relevant studies on automatic fake detection based on the DLN architectures (summarized in paragraph III-B), the main objective of these experiments is to improve the performance of the execution time, loss and accuracy by testing a new embedding process (FastText), and reducing the dimensionality of the used articles news data by assessing the impact of the first news title feature that is hypothesized as affecting the performances of automatic Fake News spotting.
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