Fake Online News

Adikpo, Janet Aver

doi:10.4018/978-1-7998-7291-7.ch005

Cited by 2 publications

(1 citation statement)

References 38 publications

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“…To serve the goal of this study, we created the COVID-19 Fake News dataset from scratch, with a subset of the Zenodo dataset [35]. Dataset collection, dataset filtering, dataset preparation and preprocessing are the three primary steps of the procedure used to create the COVID-19 Fake News dataset.…”

Section: Experimental Datasetmentioning

confidence: 99%

A Deep Learning Framework for Detection of COVID-19 Fake News on Social Media Platforms

Tashtoush

Alrababah

Darwish

et al. 2022

Data

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The fast growth of technology in online communication and social media platforms alleviated numerous difficulties during the COVID-19 epidemic. However, it was utilized to propagate falsehoods and misleading information about the disease and the vaccination. In this study, we investigate the ability of deep neural networks, namely, Long Short-Term Memory (LSTM), Bi-directional LSTM, Convolutional Neural Network (CNN), and a hybrid of CNN and LSTM networks, to automatically classify and identify fake news content related to the COVID-19 pandemic posted on social media platforms. These deep neural networks have been trained and tested using the “COVID-19 Fake News” dataset, which contains 21,379 real and fake news instances for the COVID-19 pandemic and its vaccines. The real news data were collected from independent and internationally reliable institutions on the web, such as the World Health Organization (WHO), the International Committee of the Red Cross (ICRC), the United Nations (UN), the United Nations Children’s Fund (UNICEF), and their official accounts on Twitter. The fake news data were collected from different fact-checking websites (such as Snopes, PolitiFact, and FactCheck). The evaluation results showed that the CNN model outperforms the other deep neural networks with the best accuracy of 94.2%.

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Section: Experimental Datasetmentioning

confidence: 99%