Detection of fake news in social media using CNN with grey wolf optimized BERT

Rajalakshmi  Viswanathan; Sharon Femi Paul Sunder  Nathaniel; Kala  Alwarsamy; Khanaghavalle   Rajendran

doi:10.4025/actascitechnol.v47i1.73210

Autores

Rajalakshmi Viswanathan Sri Venkateswara College of Engineering https://orcid.org/0000-0001-7920-680X
Sharon Femi Paul Sunder Nathaniel Sri Venkateswara College of Engineering https://orcid.org/0000-0002-2984-9521
Kala Alwarsamy Sri Venkateswara College of Engineering
Khanaghavalle Rajendran Sri Venkateswara College of Engineering

DOI:

https://doi.org/10.4025/actascitechnol.v47i1.73210

Palavras-chave:

BERT; N-gram; skip gram; grey wolf optimization; convolutional neural network.

Resumo

Fake news refers to stories that are falsely represented as news, that has the potential to misinform and mislead readers and identifying such fake news is a significant challenge to combat its impact in society. Our proposed work detects fake news using a hybrid method for fake news detection that uses Bidirectional Encoder Representations from Transformers (BERT) framework for feature extraction, Convolutional Neural Networks (CNN) for classification and Grey Wolf optimization for optimizing the parameters of BERT. The raw data is preprocessed and transformed into a format that can be fed as an input to the BERT model for feature extraction. This pre-trained model is fine-tuned using Grey wolf optimization (GWO) and CNN is used to classify each news article as real or fake. The proposed BERT-GWO-CNN model outperforms the existing machine learning models like Random Forest, SVM and deep learning namely RNN and LSTM in terms of prediction accuracy.

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