MINER-UVS

This repo is the released code of our work Escaping the Neutralization Effect of Modality Features Fusion in Multimodal Fake News Detection

[ Todo ] Our pre-trained model weights are comming soon.

Our released code follows to "EANN: Event Adversarial Neural Networks for Multi-Modal Fake News Detection" and "BDANN: BERT-Based Domain Adaptation Neural Network for Multi-Modal Fake News Detection"

Requirements

torch==1.12.1
cudatoolkit==11.3.1
transformers==4.27.4

Train

• Prepare the datasets Weibo and Gossip. Our datasets are from https://github.com/yaqingwang/EANN-KDD18 and https://github.com/shiivangii/SpotFakePlus, and you should put them in ./Data

• Run the python file

python ./src/run.py

• Check log files in ./log

Tips

1. When you change the dataset to run, such as changing Weibo to Gossip, you should revise

import process_weibo as process_data

to

import process_gossipcop as process_data

in line 10, run.py.

2. You should manually split the training set of GossipCop into the divisions of training and validation, then, revise the file road in the function write_data in line 89, process_gossipcop.py

Citation