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 .

Please do not hesitate to contact me if you have any questions regarding the paper and its implementation. Here is my email address.

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:

 @article{
 ghorbanpour_ramezani_fazli_rabiee_2023, 
 title = {FNR: A Similarity and transformer-based approach to detect multi-modal fake news in Social Media}, volume={13},
 author = {Ghorbanpour, Faeze and Ramezani, Maryam and Fazli, Mohammad Amin and Rabiee, Hamid R.}, 
 DOI = {10.1007/s13278-023-01065-0}, 
 number = {1}, 
 volume={13},
 pages={1--15},
 publisher={Springer},
 journal = {Social Network Analysis and Mining}, 
 year= {2023} 
 }