Official implementation of the Fake News Revealer paper
You can not select more than 25 topics Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
FaezeGhorbanpour 47c42ec9e8 lime added 4 months ago
data lime added 4 months ago
.gitignore codes added 6 months ago
LICENSE Initial commit 8 months ago
README.md readme updated 6 months ago
data_loaders.py lime added 4 months ago
evaluation.py codes added 6 months ago
image.py codes added 6 months ago
learner.py codes added 6 months ago
lime_main.py lime added 4 months ago
main.py lime added 4 months ago
model.py lime added 4 months ago
optuna_main.py lime added 4 months ago
requirements.txt codes added 6 months ago
test_main.py lime added 4 months ago
text.py codes added 6 months ago
torch_main.py lime added 4 months ago
utils.py lime added 4 months ago

README.md

Fake News Revealer

Official implementation of the “Fake News Revealer (FNR): A Similarity and Transformer-Based Approach to Detect Multi-Modal Fake News in Social Media” paper (ArXiv Link).

Requirments

FNR is built in Python 3.6 using PyTorch 1.8. Please use the following command to install the requirements:

pip install -r requirements.txt

How to Run

First, place the data address and configuration into the config file in the data directory, and then follow the train and test commands.

train

To run with Optuna for parameter tuning use this command:

python main --data "DATA NAME" --use_optuna "NUMBER OF OPTUNA TRIALS" --batch "BATCH SIZE" --epoch "EPOCHS NUMBER"

To run without parameter tuning, adjust your parameters in the config file and then use the below command:

python main --data "DATA NAME" --batch "BATCH SIZE" --epoch "EPOCHS NUMBER"

test

In the test step, at first, make sure to have the requested ‘checkpoint’ file then run the following line:

python main --data "DATA NAME" --just_test "REQUESTED TRIAL NUMBER"

Bibtex

Cite our paper using the following bibtex item:

@misc{ghorbanpour2021fnr,
      title={FNR: A Similarity and Transformer-Based Approach to Detect Multi-Modal Fake News in Social Media}, 
      author={Faeze Ghorbanpour and Maryam Ramezani and Mohammad A. Fazli and Hamid R. Rabiee},
      year={2021},
      eprint={2112.01131},
      archivePrefix={arXiv},
}