The extensive spread of fake news has the potential for extremely negative impacts on individuals and society. Therefore, fake news detection on social media has recently become an emerging research that is attracting tremendous attention.
Our proposed framework exploits the information from the news articles and the social contexts to detect fake news. The proposed model is based on a Transformer architecture, which has two parts: the encoder part to learn useful representations from the fake news data and the decoder part that predicts the future behavior based on past observations. We also incorporate many features from the news content and social contexts into our model to help us classify the news better.
In this Project I have mainly used four classification models namely :-
- Logistic Regression
- Decision Tree Classification
- Gradient Boosting Classifier
- Random Forest Classifier
- FrameWork : Jupyter Notebook
- Dataset : Click to download
- Python : 3.8.3(64-bit)
- Download the dataset from link mentioned above
- Download the jupyter file and keep all downloads in same folder
- Open folder folder in jupyter notebook
- Click kernel -> Restart and Run All
- Project will be executed
Project demo :- Fake News Detection
- Pandas
- numpy
- sklearn
- string
- re
- matplotlib