重新思考立场检测：用大模型挖掘用户层面的深层心理特征
Rethinking stance detection: A theoretically-informed research agenda for user-level inference using language models
- 从心理学视角重构立场概念，关注用户个体属性
- 利用大语言模型灵活推断用户心理特征以提升立场识别
- 适合研究社会计算、用户行为建模的学者参考
立场检测已成为自然语言处理中的热门任务，主要得益于特定目标社交媒体数据的丰富性。尽管在模型、数据集和应用方面已有大量研究，但存在两大关键缺口：一是立场缺乏理论概念化，二是多聚焦于消息级别而非用户级别。本文首先回顾了立场作为个体层面构念的跨学科起源，强调心理特征等属性对建模可能有帮助。进一步指出，预训练大语言模型（LLMs）可灵活推断用户级属性并融入立场建模。通过综述近期使用LLMs进行立场推断及用户属性融合的研究，提出一个包含四点的理论驱动型研究议程，旨在推动更具理论深度、包容性和实际影响力的立场检测研究。
原文摘要 · Abstract (English)
Stance detection has emerged as a popular task in natural language processing research, enabled largely by the abundance of target-specific social media data. While there has been considerable research on the development of stance detection models, datasets, and application, we highlight important gaps pertaining to (i) a lack of theoretical conceptualization of stance, and (ii) the treatment of stance at an individual- or user-level, as opposed to message-level. In this paper, we first review the interdisciplinary origins of stance as an individual-level construct to highlight relevant attributes (e.g., psychological features) that might be useful to incorporate in stance detection models. Further, we argue that recent pre-trained and large language models (LLMs) might offer a way to flexibly infer such user-level attributes and/or incorporate them in modelling stance. To better illustrate this, we briefly review and synthesize the emerging corpus of studies on using LLMs for inferring stance, and specifically on incorporating user attributes in such tasks. We conclude by proposing a four-point agenda for pursuing stance detection research that is theoretically informed, inclusive, and practically impactful.
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