Problem

With the rise of fake news, it has become increasingly difficult to distinguish between real and fake news. This has led to misinformation being spread, which can have serious consequences. The challenge was to develop a solution that could accurately detect fake news and prevent its spread.

We realized that traditional methods of detecting fake news were not effective enough, as they relied on manual fact-checking and were time-consuming. We needed a more sophisticated approach that could automate the process and improve accuracy.

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Solution

We decided to leverage the LIAR dataset to construct a sophisticated model incorporating convolutional filters, contextualized attention mechanisms, and a gated RNN architecture for improved performance. Our team worked tirelessly to train the model using deep learning techniques.

After several iterations, we achieved 75% binary classification accuracy and executed a six-class classification, accurately discerning varying degrees of falsity with high precision. Our solution was able to detect fake news with a high degree of accuracy and prevent its spread.

Impact

Reduced the spread of fake news by X%
Improved accuracy in detecting fake news by X%

"Quote of how incredible of an impact this had."

Team

Team Member #1 - Data Scientist
- Leveraged the LIAR dataset to construct the model
- Implemented deep learning techniques to train the model
Team Member #2 - Machine Learning Engineer
- Built the convolutional filters and gated RNN architecture for the model
- Implemented contextualized attention mechanisms for improved performance

Detecting Fake News with Advanced AI Model