Unveiling Hope in Social Media: A Multilingual Approach Using BERT

Abstract

This article presents research on the topic of protecting speech in English and Spanish on social media. The study highlights the importance of speech of hope in excellence, diversity and inclusion, and its impact on people's mental well-being and resilience. Using advanced natural language processing (NLP) techniques, including BERT and transformer models, the research develops robust methodologies for binary and multi-class speech detection tasks. The methodology includes data preprocessing, model selection, fine-tuning, training, and evaluation steps to accurately identify expressions of hope in different linguistic contexts. In addition, the paper discusses the challenges and opportunities associated with analyzing hope expression on social media, highlighting ethical considerations and practical implications for various fields such as psychology, sociology and public health. The study results show promising performance in accurately detecting hope speech in both binary and multiclass environments in English and Spanish, highlighting the potential of NLP approaches to address and promote positive communication dynamics on social media platforms.