iNews：一个用于建模个性化新闻情感反应的多模态数据集
iNews: A Multimodal Dataset for Modeling Personalized Affective Responses to News
AI总结：
本文提出 iNews 多模态新闻数据集，收集 291 名英国用户对 2,899 条新闻帖子的细粒度情感标注及标注者画像信息，证明画像信息可解释 15.2% 标注方差并提升零样本预测准确率 7%，为 LLM 个性化与情感计算研究提供新资源。
摘要：
理解个体如何感知信息并对其做出反应，是推动社会与行为科学发展以及构建以人为中心的人工智能系统的基础。当前方法往往缺乏对个性化反应进行建模所需的细粒度数据，而是依赖聚合标签，这掩盖了由个体差异驱动的丰富变异性。我们引入了 iNews，这是一个新颖的大规模数据集，专门用于促进对新闻内容的个性化情感反应建模。该数据集包含来自 291 名人口统计学上多样化的英国参与者对 2,899 条来自英国主要媒体的多模态 Facebook 新闻帖子的标注，每个样本平均有 5.18 名标注者。对于每条帖子，标注者提供多方面的标签，包括效价、唤醒度、支配度、离散情绪、内容相关性判断、分享可能性以及模态重要性评分。至关重要的是，我们收集了全面的标注者画像信息，涵盖人口统计学特征、人格、媒体信任度和消费模式，这些信息解释了 15.2% 的标注方差——显著高于现有 NLP 数据集。纳入这些信息在零样本预测中带来了 7% 的准确率提升，并且在 32 样本上下文学习条件下仍然有益。iNews 为 LLM 个性化、主观性、情感计算和人类行为模拟等领域的研究开辟了新的可能性。
英文摘要：
Understanding how individuals perceive and react to information is fundamental for advancing social and behavioral sciences and developing human-centered AI systems. Current approaches often lack the granular data needed to model these personalized responses, relying instead on aggregated labels that obscure the rich variability driven by individual differences. We introduce iNews, a novel large-scale dataset specifically designed to facilitate the modeling of personalized affective responses to news content. Our dataset comprises annotations from 291 demographically diverse UK participants across 2,899 multimodal Facebook news posts from major UK outlets, with an average of 5.18 annotators per sample. For each post, annotators provide multifaceted labels including valence, arousal, dominance, discrete emotions, content relevance judgments, sharing likelihood, and modality importance ratings. Crucially, we collect comprehensive annotator persona information covering demographics, personality, media trust, and consumption patterns, which explain 15.2% of annotation variance - substantially higher than existing NLP datasets. Incorporating this information yields a 7% accuracy gain in zero-shot prediction and remains beneficial even with 32-shot in-context learning. iNews opens new possibilities for research in LLM personalization, subjectivity, affective computing, and human behavior simulation.
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