- Keras 2.2.4
- Tensorflow 1.14.0
- NLTK 3.2.5
- Scikit-Learn 0.21.3
- Numpy 1.16.4
- Pandas 0.24.2
"Fake News" is a term used to represent fabricated news or propaganda comprising misinformation communicated through traditional media channels like print, and television as well as non-traditional media channels like social media. The general motive to spread such news is to mislead the readers, damage reputation of any entity, or to gain from sensationalism.
- Download the dataset and put it in same folder where Fake_News_Detection.ipynb is placed. Or click on the colab link, open it in playground mode and upload the downloaded files (train.xlsx, valid.xlsx and test.xlsx) in the colab.
- Run the IPython notebook section wise-
- Import all the libraries.
- Run the preprocessing section.
- For six-way classification task-
- Run 'Six-way classification section.
- For binary classification' task-
- Run 'Binary classification' section.
- I have implemented an Artificial Neural Network for both the classification tasks.
- This shallow model outperformed other complex methods mentioned in reference papers.
Please read the report for details.
- William Yang Wang. “Liar, Liar Pants on Fire”: A New Benchmark Dataset for Fake News Detection
- Tariq Alhindi, Savvas Petridis, Smaranda Muresan. Where is your Evidence: Improving Fact-checking by Justification Modeling