| 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. |
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