Computational modeling of user-generated desires on social media can significantly aid decision-makers across various fields. Initially explored through wish speech,this task has evolved into a nuanced examination of hope speech. To enhance understanding and detection, we propose a novel scheme rooted in formal semantics approaches to modality, capturing both future-oriented hopes through desires and beliefs and the counterfactuality of past unfulfilled wishes and regrets. We manually re-annotated existing hope speech datasets and built a new one which constitutes a new benchmark in the field. We also explore the capabilities of LLMs in automatically detecting hope speech, relying on several prompting strategies. To the best of our knowledge, this is the first attempt towards a language-driven decomposition of the notional category hope and its automatic detection in a unified setting.

We propose a fine-tuning strategy for English Multi-class Hope Speech Detection using Mistral, leveraging two complementary datasets: PolyHope and CDB, a new unified framework for hope speech detection. While the former provides nuanced hope-related categories such as GENERALIZED, REALISTIC, and UNREALISTIC HOPE, the later introduces linguistically grounded dimensions including COUNTERFACTUAL, DESIRE, and BELIEF. By fine-tuning Mistral on both datasets, we enable the model to capture deeper semantic representations of hope. In addition to fine-tuning, we developed advanced prompting strategies which provide interpretable, zero-shot alternatives and further inform annotation and classification designs. Our approach achieved third place in the multi-class (Macro F1=71.77) and sixth in the binary (Macro F1=85.35) settings.