The Detection of Fake Text News using a Dense-based 1D-CNN Deep Learning Algorithm
There are a lot of problems with fake news, which can make people think of things that aren't true. Social media is one of the fastest ways to get information out there because it has a big impact and can manipulate information in both good and bad ways. The goal of this paper is the optima...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
College of science, university of Diyala
2024-04-01
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| Series: | Academic Science Journal |
| Online Access: | https://acadscij.uodiyala.edu.iq/index.php/Home/article/view/450 |
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| Summary: | There are a lot of problems with fake news, which can make people think of things that aren't true. Social media is one of the fastest ways to get information out there because it has a big impact and can manipulate information in both good and bad ways. The goal of this paper is the optimal use of deep learning algorithms to solve the problem of the paper. The research problem is how accurately and to what extent can an individual distinguish between fake news articles using natural language processing and classification algorithms. What are the steps that can be taken to provide a solution?compared to the previous different methods to solve this problem, including some common deep-learning methods. In this paper, we can find fake news can be found by using the term inverse frequency document (TF-IDF) for feature extraction and a hybrid algorithm of One Dimensional-Convolutional Neural Network (1D-CNN) and Dense as the classifier. The experiments that the proposed dense-based 1D-CNN algorithm substantially outperforms other up-to-date related algorithms with an accuracy of 100%.
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| ISSN: | 2958-4612 2959-5568 |