My capstone project for BSDA degree programme at Sunway University:
Fake News Detection Using Social Media User Network and Engagement Features
For this project, FakeNewsNet/code/config.json is configured to download the required files only. Credit of data retrieval scripts and dataset goes to KaiDMML/FakeNewsNet.
(Please refer to README.md in KaiDMML/FakeNewsNet for data retrieval instructions.)
Then, data_extract.py is used to extract and clean the data to be used for this project. The output is data/final_dataset.csv.
The config file config.yaml defines the best model chosen after experimentation and model selection (in this case, Random Forest with 80% train size), and will be used to train the final model by running model.py.
To train the model from the beginning, which includes the step of model selection, remove the values of algorithm and train_size from the config.yaml file, then run model.py.
Finally, the model will be output as model.pkl.