Scholarlink

<p><b>Background:</b> Hope Speech (HpS) has been proposed as a strategy to shift online moderation from purely punitive approaches toward positive reinforcement, amplifying expressions of aspiration, resilience, and support. Despite substantial research in psychology, philosophy, and linguistics highlighting the multifaceted nature of hope, most computational models treat HpS as a coarse, binary phenomenon, neglecting the linguistic and cognitive nuances that underpin real-world hopeful discourse. <b>Objective:</b> We introduce <span>C-Hope</span>, a linguistically grounded framework for fine-grained Hope Speech Detection (HpSD) that differentiates between distinct notional categories of hope <i>(Counterfactual, Desire, Belief, Plan)</i> and considers temporal orientation, modality, and commitment level in speaker stance. Our goal is to improve the interpretability and practical utility of HpSD models, addressing the gap between broad affective categories and the complex reality of hope in discourse. <b>Methods:</b> We re-annotate existing HpS datasets with the C-Hope scheme, assembling a fine-grained, linguistically-grounded benchmark capturing degrees of speaker commitment in hope speech, comprising 4,370 English texts. C-Hope distinguishes five main groups of linguistic markers, including modal verbs, propositional attitude verbs, mood, tense, and grammatical constructions. We evaluate both fine-tuned transformer models and prompted large language models (LLMs), designing structured prompts that incorporate theoretical insights into the nature of hope. <b>Results:</b> Our findings show that models trained on the C-Hope scheme outperform binary HpS models, demonstrating improved detection of nuanced categories such as counterfactual and plan-based hope. Moreover, prompt-based LLMs leveraging structured linguistic knowledge achieve competitive results, suggesting that grounding AI models in linguistic theory is a promising avenue beyond purely data-driven approaches. <b>Conclusion:</b> Our study demonstrates the viability of a linguistically informed, multi-category framework for HpSD, paving the way for future research integrating fine-grained semantic distinctions and speaker stance modeling. The new benchmark corpus and methods provide tools for developing more transparent, ethical, and context-aware NLP systems.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Shades of Commitment in Hope Speech Detection

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月2日

查看全文
  • Tulio Ferreira Leite da Silva,
  • Gonzalo Freijedo Aduna,
  • Priya Dharshini Krishnaraj,
  • Marie Boscaro,
  • Alda Mari,
  • Farah Benamara,
  • Li Yue,
  • Jian Su

摘要

Background: Hope Speech (HpS) has been proposed as a strategy to shift online moderation from purely punitive approaches toward positive reinforcement, amplifying expressions of aspiration, resilience, and support. Despite substantial research in psychology, philosophy, and linguistics highlighting the multifaceted nature of hope, most computational models treat HpS as a coarse, binary phenomenon, neglecting the linguistic and cognitive nuances that underpin real-world hopeful discourse. Objective: We introduce C-Hope, a linguistically grounded framework for fine-grained Hope Speech Detection (HpSD) that differentiates between distinct notional categories of hope (Counterfactual, Desire, Belief, Plan) and considers temporal orientation, modality, and commitment level in speaker stance. Our goal is to improve the interpretability and practical utility of HpSD models, addressing the gap between broad affective categories and the complex reality of hope in discourse. Methods: We re-annotate existing HpS datasets with the C-Hope scheme, assembling a fine-grained, linguistically-grounded benchmark capturing degrees of speaker commitment in hope speech, comprising 4,370 English texts. C-Hope distinguishes five main groups of linguistic markers, including modal verbs, propositional attitude verbs, mood, tense, and grammatical constructions. We evaluate both fine-tuned transformer models and prompted large language models (LLMs), designing structured prompts that incorporate theoretical insights into the nature of hope. Results: Our findings show that models trained on the C-Hope scheme outperform binary HpS models, demonstrating improved detection of nuanced categories such as counterfactual and plan-based hope. Moreover, prompt-based LLMs leveraging structured linguistic knowledge achieve competitive results, suggesting that grounding AI models in linguistic theory is a promising avenue beyond purely data-driven approaches. Conclusion: Our study demonstrates the viability of a linguistically informed, multi-category framework for HpSD, paving the way for future research integrating fine-grained semantic distinctions and speaker stance modeling. The new benchmark corpus and methods provide tools for developing more transparent, ethical, and context-aware NLP systems.