Detecting Hope Across Languages: Multiclass Classification for Positive Online Discourse
This work addresses the problem of promoting positive discourse and well-being in online communities by developing multilingual, fine-grained hope speech detection models, though it is incremental as it builds on existing transformer methods.
The paper tackled multiclass hope speech detection across English, Urdu, and Spanish using XLM-RoBERTa, achieving competitive performance and significantly outperforming prior state-of-the-art models in macro F1 scores.
The detection of hopeful speech in social media has emerged as a critical task for promoting positive discourse and well-being. In this paper, we present a machine learning approach to multiclass hope speech detection across multiple languages, including English, Urdu, and Spanish. We leverage transformer-based models, specifically XLM-RoBERTa, to detect and categorize hope speech into three distinct classes: Generalized Hope, Realistic Hope, and Unrealistic Hope. Our proposed methodology is evaluated on the PolyHope dataset for the PolyHope-M 2025 shared task, achieving competitive performance across all languages. We compare our results with existing models, demonstrating that our approach significantly outperforms prior state-of-the-art techniques in terms of macro F1 scores. We also discuss the challenges in detecting hope speech in low-resource languages and the potential for improving generalization. This work contributes to the development of multilingual, fine-grained hope speech detection models, which can be applied to enhance positive content moderation and foster supportive online communities.