  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.
