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Identification of Fake News using Machine Learning and Deep Learning | IEEE Conference Publication | IEEE Xplore

Identification of Fake News using Machine Learning and Deep Learning


Abstract:

The rise of false news has emerged as a serious problem in today's society as a result of the quick expansion of online social media platforms and the simplicity of infor...Show More

Abstract:

The rise of false news has emerged as a serious problem in today's society as a result of the quick expansion of online social media platforms and the simplicity of information distribution. Spreading false information can have negative effects such as social unrest, political polarization, and a loss of faith in media sources. A dataset with both real and false news is being created for the study. To improve the quality of the data, preprocessing techniques including Port Stemmer, and stop-word removal are used. This study uses ML and deep learning algorithms to identify bogus news on the internet, including Naive Bayes, Passive Aggressive, Logistics Regression, BERT, RNN, and LSTM. According to our research, the LSTM has a 99.99% accuracy rate. To determine whether the suggested strategy is effective, experimental assessments and performance comparisons are made. In order to increase the classification models' precision, recall, and accuracy, the research also examines feature engineering and model optimisation techniques.
Date of Conference: 23-24 June 2023
Date Added to IEEE Xplore: 02 October 2023
ISBN Information:
Conference Location: Gorakhpur, India

References

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