Evaluating BERT-based Pre-training Language Models for Detecting Misinformation

TL;DR

This paper explores the use of BERT-based pre-trained language models for automated misinformation detection, demonstrating superior performance over existing methods across various datasets and training sizes.

Contribution

It introduces a BERT-based approach for misinformation detection and compares different language models' performance with varying trainable parameters.

Findings

BERT-based models outperform state-of-the-art techniques.

Larger datasets improve detection accuracy.

Pre-trained models effectively encode text for classification.

Abstract

It is challenging to control the quality of online information due to the lack of supervision over all the information posted online. Manual checking is almost impossible given the vast number of posts made on online media and how quickly they spread. Therefore, there is a need for automated rumour detection techniques to limit the adverse effects of spreading misinformation. Previous studies mainly focused on finding and extracting the significant features of text data. However, extracting features is time-consuming and not a highly effective process. This study proposes the BERT- based pre-trained language models to encode text data into vectors and utilise neural network models to classify these vectors to detect misinformation. Furthermore, different language models (LM) ' performance with different trainable parameters was compared. The proposed technique is tested on different short and long text datasets. The result of the proposed technique has been compared with the state-of-the-art techniques on the same datasets. The results show that the proposed technique performs better than the state-of-the-art techniques. We also tested the proposed technique by combining the datasets. The results demonstrated that the large data training and testing size considerably improves the technique's performance.