Fake News Stance Detection Using Deep Learning Architecture (CNN-LSTM)

Society and individuals are negatively influenced both politically and socially by the widespread increase of fake news either way generated by humans or machines. In the era of social networks, the quick rotation of news makes it challenging to evaluate its reliability promptly. Therefore, automate...

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Main Authors: Muhammad Umer, Zainab Imtiaz, Saleem Ullah, Arif Mehmood, Gyu Sang Choi, Byung-Won On
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Online Access:https://ieeexplore.ieee.org/document/9178321/
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author Muhammad Umer
Zainab Imtiaz
Saleem Ullah
Arif Mehmood
Gyu Sang Choi
Byung-Won On
author_facet Muhammad Umer
Zainab Imtiaz
Saleem Ullah
Arif Mehmood
Gyu Sang Choi
Byung-Won On
author_sort Muhammad Umer
collection DOAJ
description Society and individuals are negatively influenced both politically and socially by the widespread increase of fake news either way generated by humans or machines. In the era of social networks, the quick rotation of news makes it challenging to evaluate its reliability promptly. Therefore, automated fake news detection tools have become a crucial requirement. To address the aforementioned issue, a hybrid Neural Network architecture, that combines the capabilities of CNN and LSTM, is used with two different dimensionality reduction approaches, Principle Component Analysis (PCA) and Chi-Square. This work proposed to employ the dimensionality reduction techniques to reduce the dimensionality of the feature vectors before passing them to the classifier. To develop the reasoning, this work acquired a dataset from the Fake News Challenges (FNC) website which has four types of stances: agree, disagree, discuss, and unrelated. The nonlinear features are fed to PCA and chi-square which provides more contextual features for fake news detection. The motivation of this research is to determine the relative stance of a news article towards its headline. The proposed model improves results by ~4% and ~20% in terms of $Accuracy$ and $F1-score$ . The experimental results show that PCA outperforms than Chi-square and state-of-the-art methods with 97.8% accuracy.
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spelling doaj.art-61bae30e90604c9eb4434ffbc4212e142022-12-21T19:59:41ZengIEEEIEEE Access2169-35362020-01-01815669515670610.1109/ACCESS.2020.30197359178321Fake News Stance Detection Using Deep Learning Architecture (CNN-LSTM)Muhammad Umer0https://orcid.org/0000-0002-6015-9326Zainab Imtiaz1https://orcid.org/0000-0002-4299-8361Saleem Ullah2https://orcid.org/0000-0003-3747-1263Arif Mehmood3https://orcid.org/0000-0001-5822-4005Gyu Sang Choi4https://orcid.org/0000-0002-0854-768XByung-Won On5Department of Computer Science, Khwaja Fareed University of Engineering and Information Technology, Rahim Yar Khan, PakistanDepartment of Computer Science, Khwaja Fareed University of Engineering and Information Technology, Rahim Yar Khan, PakistanDepartment of Computer Science, Khwaja Fareed University of Engineering and Information Technology, Rahim Yar Khan, PakistanDepartment of Computer Science and Information Technology, The Islamia University of Bahawalpur, Bahawalpur, PakistanDepartment of Information and Communication Engineering, Yeungnam University, Gyeongsan, South KoreaDepartment of Statistics and Computer Science, Kunsan National University, Gunsan, South KoreaSociety and individuals are negatively influenced both politically and socially by the widespread increase of fake news either way generated by humans or machines. In the era of social networks, the quick rotation of news makes it challenging to evaluate its reliability promptly. Therefore, automated fake news detection tools have become a crucial requirement. To address the aforementioned issue, a hybrid Neural Network architecture, that combines the capabilities of CNN and LSTM, is used with two different dimensionality reduction approaches, Principle Component Analysis (PCA) and Chi-Square. This work proposed to employ the dimensionality reduction techniques to reduce the dimensionality of the feature vectors before passing them to the classifier. To develop the reasoning, this work acquired a dataset from the Fake News Challenges (FNC) website which has four types of stances: agree, disagree, discuss, and unrelated. The nonlinear features are fed to PCA and chi-square which provides more contextual features for fake news detection. The motivation of this research is to determine the relative stance of a news article towards its headline. The proposed model improves results by ~4% and ~20% in terms of $Accuracy$ and $F1-score$ . The experimental results show that PCA outperforms than Chi-square and state-of-the-art methods with 97.8% accuracy.https://ieeexplore.ieee.org/document/9178321/Fake news detectiontext miningdeep learningPCAChi-squareCNN-LSTM
spellingShingle Muhammad Umer
Zainab Imtiaz
Saleem Ullah
Arif Mehmood
Gyu Sang Choi
Byung-Won On
Fake News Stance Detection Using Deep Learning Architecture (CNN-LSTM)
IEEE Access
Fake news detection
text mining
deep learning
PCA
Chi-square
CNN-LSTM
title Fake News Stance Detection Using Deep Learning Architecture (CNN-LSTM)
title_full Fake News Stance Detection Using Deep Learning Architecture (CNN-LSTM)
title_fullStr Fake News Stance Detection Using Deep Learning Architecture (CNN-LSTM)
title_full_unstemmed Fake News Stance Detection Using Deep Learning Architecture (CNN-LSTM)
title_short Fake News Stance Detection Using Deep Learning Architecture (CNN-LSTM)
title_sort fake news stance detection using deep learning architecture cnn lstm
topic Fake news detection
text mining
deep learning
PCA
Chi-square
CNN-LSTM
url https://ieeexplore.ieee.org/document/9178321/
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