Detecting Hope Across Languages: Multiclass Classification for Positive Online Discourse

• URL: http://arxiv.org/abs/2509.25752v1
• Date: Tue, 30 Sep 2025 04:16:28 GMT
• Title: Detecting Hope Across Languages: Multiclass Classification for Positive Online Discourse
• Authors: T. O. Abiola, K. D. Abiodun, O. E. Olumide, O. O. Adebanji, O. Hiram Calvo, Grigori Sidorov,
• Abstract summary: We present a machine learning approach to multiclass hope speech detection across multiple languages, including English, Urdu, and Spanish.<n>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.<n>Our proposed methodology is evaluated on the PolyHope dataset for the PolyHope-M 2025 shared task, achieving competitive performance across all languages.
• Score: 4.905674855734124
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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