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CGRN4FakeNews: Convolutional Gated Recurrent Network for Fake News Detection

ผศ.ดร.นิกร กรรณิกากลาง | Fake News deep Learning text classification

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CGRN4FakeNews: Convolutional Gated Recurrent Network for Fake News Detection

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Collection	RMUTK Research Repository (RMUTK IR) บทความประชุมวิชาการ — Proceedings
ID RMUTK Digital	RMUTK000049
Title	CGRN4FakeNews: Convolutional Gated Recurrent Network for Fake News Detection
Alternative title	CGRN4FakeNews: Convolutional Gated Recurrent Network for Fake News Detection
Authors	ผศ.ดร.นิกร กรรณิกากลาง ผู้แต่งหลัก
Faculty	คณะบริหารธุรกิจ
Volume / Issue / Pages	pp.49-56
Conference	12th International Conference on Creative Technology (CreTech2026)
Conference Date	2026-08-08
Conference Location	โรงแรมแกรนด์ พาลาสโซ่ พัทยา จังหวัดชลบุรี
Year	2569
Level	ระดับนานาชาติ
DOI	-
ISBN / ISSN	-
Funding Source	มทร.กรุงเทพ
Abstract	This study aims to (1) examine the effectiveness of the Convolutional Gated Recurrent Network for Fake News Detection (CGRN4FakeNews) model in fake news classification and (2) compare its performance with baseline methods across different datasets. The model was evaluated using the PolitiFact and GossipCop subsets of the FakeNewsNet dataset. The research process involved text preprocessing and data cleaning, including word tokenization, lowercasing, the removal of URLs, HTML tags, numerical values, punctuation marks, and redundant whitespace, as well as stopword handling. The processed text data were then transformed into vector representations using a word embedding technique for model training and evaluation. The model performance was assessed based on Accuracy, Precision, Recall, F1-score, and Confusion Matrix metrics. The experimental results indicate that the proposed CGRN4FakeNews model effectively distinguishes between real and fake news articles, achieving classification accuracies of 95.75% on the PolitiFact dataset and 91.12% on the GossipCop dataset. These findings demonstrate the effectiveness and suitability of the CGRN4FakeNews model for fake news detection tasks.
Abstract (EN)	This study aims to (1) examine the effectiveness of the Convolutional Gated Recurrent Network for Fake News Detection (CGRN4FakeNews) model in fake news classification and (2) compare its performance with baseline methods across different datasets. The model was evaluated using the PolitiFact and GossipCop subsets of the FakeNewsNet dataset. The research process involved text preprocessing and data cleaning, including word tokenization, lowercasing, the removal of URLs, HTML tags, numerical values, punctuation marks, and redundant whitespace, as well as stopword handling. The processed text data were then transformed into vector representations using a word embedding technique for model training and evaluation. The model performance was assessed based on Accuracy, Precision, Recall, F1-score, and Confusion Matrix metrics. The experimental results indicate that the proposed CGRN4FakeNews model effectively distinguishes between real and fake news articles, achieving classification accuracies of 95.75% on the PolitiFact dataset and 91.12% on the GossipCop dataset. These findings demonstrate the effectiveness and suitability of the CGRN4FakeNews model for fake news detection tasks.
Keywords (TH)	Fake News deep Learning text classification
Keywords (EN)	Fake News deep Learning text classification
Access Level	Open Access (เปิดสาธารณะ)

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RMUTK000049_full.pdf

จำนวนดาวน์โหลด: 22 ครั้ง

Open Access

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