Fake News Challenge

Prajwal Rao (5176504) & Julian Blair (3463793) | COMP9417 2018s1

Acknowledgement

The Fake News Challenge was hosted in 2017 by a group of academic and industry volunteers. Learn more about the challenge here.

Thanks to the same team for providing a baseline implementation, which was used as a starting point for our project. The GitHub repository for the baseline can be found here.

Install

Python packages can be installed via commandline using:

pip install [packagename]

NLTK packages can be installed within Python using:

import nltk nltk.download('[packagename]')

Prerequisites

• Python 3
• pip packages
  • numpy
  • scipy
  • sklearn
  • tqdm
  • nltk (see below)
• nltk packages
  • punkt
  • wordnet
  • averaged_perceptron_tagger

Navigation

The data folder contains the CSVs provided for the challenge as training data, testing data, and competition benchmarking data.

The src folder contains the baseline code provided, as well as implementations built upon the baseline by us.

Source subfolders

Subfolder	Version	Description
baseline	0	The baseline provided.
word_overlap	1	Restructures the classificaiton problem from multi-class to multi-tier two-class, and modifies the word_overlap feature to filter common words.
paraphrasing	2	Adds lexical dimensionality-reduction to existing n-gram features.
final	3	Adds Naive Bayes classifier and all-caps frequency feature.

Each source subfolder contains the following files and folders:

Item	Description
fnc_kfold.py	The main execution script. Extracts CSVs, generates data splits for training/testing, precomputes features, then fits the hold-out set and test data.
feature_engineering.py	Contains helper functions for feature precomputation.
features/	Contains precomputed feature files for later use in fitting the provided data.
utils/	Contains helper functions provided in baseline for dataset generation, test split creation, and scoring.

Execution

To run the feature generation and scoring process, navigate to any of these source subfolders and run the following commnad:

python|python3 fnc_kfold.py