cover of episode DIY Fake News Detector: Unmask misinformation with Recurrent Neural Networks

DIY Fake News Detector: Unmask misinformation with Recurrent Neural Networks

2024/7/26
logo of podcast Machine Learning Tech Brief By HackerNoon

Machine Learning Tech Brief By HackerNoon

Frequently requested episodes will be transcribed first

Shownotes Transcript

This story was originally published on HackerNoon at: https://hackernoon.com/diy-fake-news-detector-unmask-misinformation-with-recurrent-neural-networks). Explore the power of RNNs in fake news detection, from data preprocessing to model evaluation, showcasing their potential to combat misinformation. Check more stories related to machine-learning at: https://hackernoon.com/c/machine-learning). You can also check exclusive content about #deep-learning), #fake-news), #machine-learning), #lstm), #rnn), #misinformation), #fake-news-detector), #recurrent-neural-networks), and more.

        This story was written by: [@kisican](https://hackernoon.com/u/kisican)). Learn more about this writer by checking [@kisican's](https://hackernoon.com/about/kisican)) about page,
        and for more stories, please visit [hackernoon.com](https://hackernoon.com)).
        
            
            
            Though challenging, it is equally rewarding to be in a position to build a fake news detection system using RNNs. This code will walk you through the stage of data preprocessing to model evaluation. The power of RNNs, especially LSTMs, is utilised while decoding sequential data to make a distinction between real and fake news. If we could fine-tune these models and get hold of global news datasets, AI can then be core in battling misinformation.