COVID-19 Fake News Detection using Deep Learning Model

Akhter, Mahabuba; Hossain, Syed Md. Minhaz; Nigar, Rizma Sijana; Paul, Srabanti; Kamal, Khaleque Md. Aashiq; Sen, Anik; Sarker, Iqbal H.

doi:10.1007/s40745-023-00507-y

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2024

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Cited by 3 publications

References 32 publications

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Detecting Fake News Using Machine Learning Techniques

Beri,

Sharma

2024

2024 Second International Conference on Intelligent Cyber Physical Systems and Internet of Things (ICoICI)

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Detecting Fake News Using Machine Learning Techniques

Beri,

Sharma

2024

2024 Second International Conference on Intelligent Cyber Physical Systems and Internet of Things (ICoICI)

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A Novel Content-based Approach for Fake News Detection using Transformer Model: A Case Study of Covid-19 Dataset

Bahmanyar,

Miyaneh

2024

2024 10th International Conference on Web Research (ICWR)

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BERTGuard: Two-Tiered Multi-Domain Fake News Detection with Class Imbalance Mitigation

Alnabhan,

Branco

2024

BDCC

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In an era where misinformation and fake news undermine social well-being, this work provides a complete approach to multi-domain fake news detection. Multi-domain news refers to handling diverse content across various subject areas such as politics, health, research, crime, and social concerns. Recognizing the lack of systematic research in multi-domain fake news detection, we present a fundamental structure by combining datasets from several news domains. Our two-tiered detection approach, BERTGuard, starts with domain classification, which uses a BERT-based model trained on a combined multi-domain dataset to determine the domain of a given news piece. Following that, domain-specific BERT models evaluate the correctness of news inside each designated domain, assuring precision and reliability tailored to each domain’s unique characteristics. Rigorous testing on previously encountered datasets from critical life areas such as politics, health, research, crime, and society proves the system’s performance and generalizability. For addressing the class imbalance challenges inherent when combining datasets, our study rigorously evaluates the impact on detection accuracy and explores handling alternatives—random oversampling, random upsampling, and class weight adjustment. These criteria provide baselines for comparison, fortifying the detection system against the complexities of imbalanced datasets.

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COVID-19 Fake News Detection using Deep Learning Model

Cited by 3 publications

References 32 publications

Detecting Fake News Using Machine Learning Techniques

Detecting Fake News Using Machine Learning Techniques

A Novel Content-based Approach for Fake News Detection using Transformer Model: A Case Study of Covid-19 Dataset

BERTGuard: Two-Tiered Multi-Domain Fake News Detection with Class Imbalance Mitigation

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