The overwhelming abundance of data has created a misinformation crisis. Unverified sensationalism that is designed to grab the readers' short attention span, when crafted with malice, has caused irreparable damage to our society's structure. As a result, determining the reliability of an article has become a crucial task. After various ablation studies, we propose a multi-input model that can effectively leverage both tabular metadata and post content for the task. Applying state-of-the-art finetuning techniques for the pretrained component and training strategies for our complete model, we have achieved a 0.9462 ROC-score on the VLSP private test set.
@article{arxiv.2109.12777,
title = {ReINTEL Challenge 2020: A Comparative Study of Hybrid Deep Neural Network for Reliable Intelligence Identification on Vietnamese SNSs},
author = {Hoang Viet Trinh and Tung Tien Bui and Tam Minh Nguyen and Huy Quang Dao and Quang Huu Pham and Ngoc N. Tran and Ta Minh Thanh},
journal= {arXiv preprint arXiv:2109.12777},
year = {2021}
}