首个句子级社交感知标注数据集，分析文本中的温暖与能力维度。
Annotating Dimensions of Social Perception in Text: A Sentence-Level Dataset of Warmth and Competence
- 构建包含1600+句对的社交感知标注数据集
- 评估大模型在识别信任、亲和力和能力上的表现
- 适合研究社会认知与NLP交叉的学者使用
温暖（W）（常进一步分解为信任（T）和亲和力（S））与能力（C）是人们评价个体和社会群体的核心维度（Fiske, 2018）。尽管这些概念在社会心理学中已确立，但在自然语言处理（NLP）领域仍处于起步阶段，现有研究多限于词级词典，难以捕捉其在长文本和话语中的语境表达。本文提出温暖与能力句子数据集（W&C-Sent），这是首个针对句子层面温暖与能力维度的标注数据集。该数据集包含超过1600个英文句子-目标对，由社交媒体帖子构成，表达对特定个体或社会群体的态度与观点。我们详细描述了数据收集、标注及质量控制流程，并评估多种大语言模型（LLMs）在识别信任、亲和力和能力方面的表现。W&C-Sent为语言中温暖与能力的分析提供了新资源，推动了NLP与计算社会科学的交叉研究。
原文摘要 · Abstract (English)
Warmth (W) (often further broken down intoTrust (T) and Sociability (S)) and Competence (C) are central dimensions along which people evaluate individuals and social groups (Fiske, 2018). While these constructs are well established in social psychology, they are only starting to get attention in NLP research through word-level lexicons, which do not fully capture their contextual expression in larger text units and discourse. In this work, we introduce Warmth and Competence Sentences (W&C-Sent), the first sentence-level dataset annotated for warmth and competence. The dataset includes over 1,600 English sentence--target pairs annotated along three dimensions: trust and sociability (components of warmth), and competence. The sentences in W&C-Sent are social media posts that express attitudes and opinions about specific individuals or social groups (the targets of our annotations). We describe the data collection, annotation, and quality-control procedures in detail, and evaluate a range of large language models (LLMs) on their ability to identify trust, sociability, and competence in text. W&C-Sent provides a new resource for analyzing warmth and competence in language and supports future research at the intersection of NLP and computational social science.
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