We present the Annotated Reddit Conversation Corpus (ARCC), an English-language dataset of online discussions annotated for Speech Acts and Functional Dependence Relations, designed to investigate how varying degrees of subjectivity influence conversational dynamics and interaction patterns. At the speech act level, we distinguish factual from opinion statements and further classify opinions along a five-degree scale of subjectivity. Functional Dependence Relations capture how segments relate to preceding ones. Analyses show that opinion-discussion contexts feature frequent inter-subjective opinions eliciting explicit agreement and disagreement, while information-exchange contexts exhibit less subjective opinions with responses like answers or requests for clarification. We further demonstrate that a transformer model can predict the subjectivity scale with promising performance. The corpus and annotation guidelines are made available to support future research on opinion expression and automated dialogue analysis.

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.

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.