Human Reliability Analysis using AI

Abstract

The exponential growth of social media platforms has revolutionized the way information is shared and consumed, leading to the widespread dissemination of both factual and false information. The rapid spread of misleading or entirely false news poses a significant threat to public discourse and the integrity of democratic processes. The task of accurately classifying the truthfulness of statements is complex, particularly due to the nuanced and often ambiguous nature of content shared on social media. Traditional Natural Language Processing (NLP) techniques, such as Bidirectional Encoder Representations from Transformers (BERT), have demonstrated proficiency in contextual understanding and text classification. However, these approaches frequently encounter limitations in accuracy, largely due to their difficulties in managing imbalanced datasets and the lack of integration with supplementary feature sets. To address these challenges, this research proposes a novel hybrid model that combines the strengths of BERT with dependency parsing and integrates a Deep Learning (DL) model designed to process metadata. This hybrid approach enhances the model's ability to accurately analyze and classify the complex and varied structures within the dataset, leading to improved overall accuracy. Additionally, this study explores different network architectures and preprocessing techniques aimed at optimizing the model's performance. The proposed hybrid model was tested on the LIAR dataset, achieving a notable 64.6% accuracy, which represents a 13.8% improvement over the previous leading method, the Fake News Detection Multi-Task Learning (FDML) model. The findings from this research indicate that the incorporation of richer linguistic features and metadata into classification models can significantly enhance the effectiveness of fake news detection and categorization on social media platforms.