Abstract
The rise of sensationalism in news reporting, driven by market saturation and online competition, has compromised news quality and trust. At its core, sensationalism aims to evoke affective responses in readers. Current NLP approaches to emotion detection predominantly focus on the writer's perspective and overlook the subjective affective experience across groups and individuals, relying on aggregation techniques that obscure important nuances in reader reactions. To address this gap, we introduce iNews, a novel large-scale dataset capturing subjective affective responses to news headlines from reader's perspective. Our dataset comprises annotations from 291 participants across 2,900 Facebook news posts from major UK outlets, with an average of 5.18 annotators per sample. For each post, annotators provide multifaceted responses including valence, arousal, dominance, discrete emotion labels, content relevance, sharing likelihood, and modality importance (text, image, or both). Furthermore, we collect rich persona variables covering demographics, personality traits, media trust, and consumption patterns. iNews will enable more accurate modeling of affective responses by considering both individual and contextual factors, advancing pluralistic alignment and diverse representation in language technology.