In this hands-on project, I will train a Long Short Term Memory (LSTM) network to detect fake news from a given news corpus. This project could be practically used by media companies to automatically predict whether the circulating news is fake or not. The process could be done automatically without having humans manually review thousands of news-related articles.
Dataset from kaggle dataset.
Acknowledgements
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Ahmed H, Traore I, Saad S. “Detecting opinion spams and fake news using text classification”, Journal of Security and Privacy, Volume 1, Issue 1, Wiley, January/February 2018.
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Ahmed H, Traore I, Saad S. (2017) “Detection of Online Fake News Using N-Gram Analysis and Machine Learning Techniques. In: Traore I., Woungang I., Awad A. (eds) Intelligent, Secure, and Dependable Systems in Distributed and Cloud Environments. ISDDC 2017. Lecture Notes in Computer Science, vol 10618. Springer, Cham (pp. 127-138).
Inspiration
Can you use this data set to make an algorithm able to determine if an article is fake news or not ?
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Understand the problem statement and business case
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Import libraries/datasets and perform preliminary data processing
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Perform Exploratory data analysis
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Perform data cleaning
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Visualize datasets
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Prepare the data by performing tokenization and padding
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Understand the intuition behind recurrent neural networks
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Understand the intuition behind LSTMs
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Train an LSTM Model
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Assess/evaluate trained model performance
- Clone the repo
https://github.com/nqkhanh2002/Fake-News-Detection-with-Machine-Learning
- Run the jupyter notebook Notebook will automatically download data to your device. During notebook execution, use the package installer for Python to install packages that you are missing.
- Facebook : Nguyễn Quốc Khánh
- Email : nqkdeveloper@gmail.com