Automatic Detection of Fake News in the Syrian War

This repository contains the code and input needed to reproduce our work.

feature_extraction, and learning_model are the source code directories that contain the code behind the work

main.py is the file needed to reproduce our work.

Testing feature extraction:

In order to test the feature extraction approach in our work, you need to first download the 'stanford-corenlp-full-2018-02-27' Stanford dependency parser which should consist of the following:

1. stanford-corenlp-3.9.1.jar
2. stanford-corenlp-3.9.1-models.jar

It can be downloaded using the following link: http://nlp.stanford.edu/software/stanford-corenlp-full-2018-02-27.zip

You will also need to download the StanfordNERTagger 'stanford-ner-2018-02-27' which consists of the following:

1. classifiers/english.all.3class.distsim.crf.ser.gz
2. stanford-ner.jar

It can be downloaded using the following link: https://nlp.stanford.edu/software/stanford-ner-2018-02-27.zip

Download the above two requirements and save them in this directory.

Inside main.py, uncomment test_feature_extraction() from the main function in order to test feature extraction. Feature extraction csv files will be outputted into the output directory

Testing machine learning models:

By running the main.py function, you run the best found machine learning model on the FA-KES train and test datasets

You can change the paths in the test_features_model() function in the main.py file in order to test it on other datasets. The other datasets are found in the other_datasets directory (Buzzfeed train and test features extraction are there)

comment the test_features_model() in the main function call in the main.py file if you do not want to test the feature-based machine learning model.

Testing baseline text-based deep learning model:

You need to download the Glove file glove.6B.300d.txt and place it in the same directory as the main.py file

It can be downloaded from this link: http://nlp.stanford.edu/data/glove.6B.zip

uncomment the test_text_model() in the main function call in the main.py file if you want to test the text-based deep learning model.

You can change the paths in the test_text_model() function in the main.py file in order to test it on other datasets. The other datasets are found in the other_datasets directory (Buzzfeed train and test features extraction are there)