Fake News Detection System Using Machine Learning and Deep Learning
Velumuru Srikanth1, Metta Hari Krishna Bhatlu2, Madina Yashoda3, Kondapu Hari Krishna4
,Karaka Jyothi Swaroop5
1Assistant Professor, CSE
[2-5]B.Tech Students
[1,2,3,4,5]Department of Computer Science and Engineering, Raghu Engineering College, Vishakapatnam
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ABSTRACT
The rise of fake news in today's age of mass media poses a serious threat to the credibility of the media and public discourse [1]. This project focuses on developing effective and robust solutions to detect fake news by integrating multiple machine learning algorithms. This project uses a variety of techniques, including natural language processing (NLP), clustering, classification algorithms and deep learning, to identify the content of patterns that indicate error messages. The project first explores NLP algorithms to extract content from news media content. These features allow the system to understand and classify the language used, helping to identify language patterns associated with fake news. Additionally, emotional analysis plays an important role in visualizing the tone and content of the text, further increasing the accuracy of the findings. The integration method is used to combine various machine learning models. By combining the performance of various algorithms, the system aims to overcome individual limitations and provide a more comprehensive and effective evaluation of media content. This approach helps increase the power of fake news detection and adapts to different fraud strategies. The project also includes deep learning algorithms, especially neural networks, to capture relationships in data. The use of deep learning ensures that the system can recognize nuances in the language and clarifies the complex information of the error message. Continuous updating and reworking of machine learning models is an important part of the process, allowing the system to constantly adapt to fraud strategies [2]. The aim of fake news detection system is to create a well-informed, weak search for fake news that can help create greater awareness and public approval.
Key Words: Fake news detection, Machine learning techniques, Advanced algorithms, Classification algorithms, Deep learning, Feature extraction, Model training, Natural language processing
