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Cross-SEAN

This repository contains the code and data used in the paper titled 'Cross-SEAN: A cross-stitch semi-supervised neural attention model for COVID-19 fake news detection'

link: https://www.sciencedirect.com/science/article/abs/pii/S1568494621003161

README is divided into 3 files.

Contents

  • data.zip - Contains sample data (2000 Fake, 2000 Genuine and 5000 Unlabelled) according to the format expected by the pipeline.
  • covid19.sh - Shell config file which ensures the end-to-end working.
  • extract_features.py - Prepares the data by extracting various inputs from the tweet objects containing json files.
  • preprocess.py - Prepares all the data according to the embedding vector inputs.
  • model.py - Contains all the model component classes
  • training.py - Runs the training epochs and evaluation steps
  • main.py - Loads all the data and initialises training, followed by evaluation
  • config.py - Contains all args which are parsed after running main.py, along with default values.
  • flask_sean.py - Responsible for the chrome extension API (to run, replace your Tweepy credentials in the file)
  • sentimentanalysis - A helper module to generate some tweet features in preprocess.py. Uses a slightly modified version of williamscott701/sentimentanalysis to create the extended version of the data.

Compute Requirements:

  • 5GB GPU for training
  • 1GB for inference

How to run on test?

  • Unzip data.zip from Sample CTF Dataset folder OR
  • Add/Update data in data/covid19/ in json format, as fake.json, genuine.json, unlabelled.json. (sample data given in CTF are given here in json format)
  • Install the requirements pip install -r requirements.txt
  • Change the project and directory name in covid19.sh (optional)
  • Run the pipeline (changing the paths appropriately, if any of the above steps are done differently) sh ./covid19.sh

Note

  • To run live service, follow README_Chrome_SEAN.txt
  • Separate readme are available for other aspects.

Acknowledgements

Please cite the paper if you are using the dataset or the model.

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