Unveiling Hope in Social Media: A Multilingual Approach using BERT

Abstract This article presents research on the topic of detecting hope speech in English and Spanish on social media platforms. The study explores the significance of hope speech in fostering equality, diversity, and inclusion, as well as its implications for individuals’ mental well-being and resilience. Leveraging advanced natural language processing (NLP) techniques, including BERT and transformer models, the research develops robust methodologies for binary and multiclass hope speech detection tasks. The methodology encompasses data preprocessing, model selection, fine-tuning, training, and evaluation stages, aiming to accurately identify expressions of hope across diverse linguistic contexts. Furthermore, the paper discusses the challenges and opportunities associated with analyzing hope speech on social networks, emphasizing the ethical considerations and practical implications for various fields, such as psychology, sociology, and public health. The results demonstrate promising performance in accurately detecting hope speech in both binary and multiclass settings across English and Spanish languages, underscoring the potential of NLP approaches in understanding and promoting positive communication dynamics on social media platforms.