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dc.contributor.author | Rafi Ullah Khan, 01-242182-008 | |
dc.date.accessioned | 2022-12-23T12:15:11Z | |
dc.date.available | 2022-12-23T12:15:11Z | |
dc.date.issued | 2021 | |
dc.identifier.uri | http://hdl.handle.net/123456789/14541 | |
dc.description | Supervised by Dr. Shehzad Khalid | en_US |
dc.description.abstract | Classification of fake news content is one of the challenging problems of Natural Language Processing (NLP). In classification of fake news detection, the classes of true and fake news are predefined in which the news are assigned based on model’s judgement. The increase in social media gave an edge to spreading of fake news easily. it has now become one of the considerable menaces to journalism, democracy and freedom of expression. In this era, fake news has emerged as a world topic, and it has become of major concern for the people to know the authenticity of a news content over the social media. The existing contentbased approaches such as rule based, probabilistic and machine learning are used for classification. These approaches are far from achieving acceptable accuracy with fake news detection due to the complex nature of the news content that is generated to mislead the audience. These models require handcrafted features, which has the possibility of missing out the important features or considering the unimportant features. Secondly, these traditional models lack the ability of memory element to keep the track of previous words as well as current appearing words also known as words dependency, which is one of great importance in the classification of fake news. In this research, we have proposed classification of fake news model that comprise of news content representation scheme also known word embeddings and deep learning model that represent the news articles as latent features of the text. The proposed model for classification of fake news is a blend of two DL models consisting of 1D Convolution Neural Network (CNN) as feature extractor and LSTM as classification model. Our model outperforms the state of art by a well-off accuracy on three known datasets of fake news detection such as 98.6% on FakeNewsNet datset, 97.30% on ISOT dataset and 90.08% on FA-KES dataset. | en_US |
dc.language.iso | en | en_US |
dc.publisher | Computer Engineering, Bahria University Engineering School Islamabad | en_US |
dc.relation.ispartofseries | MS(CE);T-1860 | |
dc.subject | Computer Engineering | en_US |
dc.title | Fake News Detection Using Deep Learning | en_US |
dc.type | MS Thesis | en_US |