ReINTEL Challenge 2020: A Comparative Study of Hybrid Deep Neural Network for Reliable Intelligence Identification on Vietnamese SNSs
This addresses the misinformation crisis on Vietnamese social networks, but it is incremental as it applies existing techniques to a specific domain.
The paper tackled the problem of identifying reliable intelligence on Vietnamese social networks by proposing a multi-input model that leverages both tabular metadata and post content, achieving a 0.9462 ROC-score on the VLSP private test set.
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.