@inproceedings{zheng-etal-2026-beyond-retweet,
title = "Beyond the Retweet: A Large-Scale Dataset of Reader Emotion, Propagation Intent, and Behavior",
author = "Zheng, Yujin and
Liao, Jian and
Wang, Suge and
Zheng, JianXing and
Li, Deyu and
Chen, Qian",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.841/",
pages = "19506--19542",
ISBN = "979-8-89176-459-0",
abstract = "Current emotion analysis in social media is predominantly author-centric, failing to capture the subjective nature of emotional responses across diverse readers. This paradigm overlooks the crucial link between individual perception, implicit propagation intent, propagation behavior, and the underlying social network. To bridge this gap, we introduce EmoIntenNet, a novel bilingual (English and Chinese) large-scale benchmark that uniquely integrates six critical dimensions for social perception: author-created content, actual readers' emotional feedback (derived from their comments), implicit propagation intent, explicit propagation behavior, user attributes, and the social relationship graph. This benchmark advances the shift towards more granular, reader-centric analysis, where different readers' emotional responses to the same content are grounded in their actual social interactions rather than simulated annotations. By annotating implicit emotions from reader comments and synchronously capturing implicit multi-label propagation intents, EmoIntenNet provides a unique resource to model the intrinsic triadic coupling among implicit emotion, implicit intent, and explicit behavior, grounded in social context. We establish a comprehensive evaluation protocol, testing state-of-the-art methods, including large language models (LLMs) with advanced reasoning enhancement. Our findings reveal significant shortcomings in existing approaches when handling this multifaceted, user-aware task. EmoIntenNet offers a foundational resource and clear direction for future research in socially-intelligent NLP, pushing models towards a more unified understanding of emotion on social media."
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<abstract>Current emotion analysis in social media is predominantly author-centric, failing to capture the subjective nature of emotional responses across diverse readers. This paradigm overlooks the crucial link between individual perception, implicit propagation intent, propagation behavior, and the underlying social network. To bridge this gap, we introduce EmoIntenNet, a novel bilingual (English and Chinese) large-scale benchmark that uniquely integrates six critical dimensions for social perception: author-created content, actual readers’ emotional feedback (derived from their comments), implicit propagation intent, explicit propagation behavior, user attributes, and the social relationship graph. This benchmark advances the shift towards more granular, reader-centric analysis, where different readers’ emotional responses to the same content are grounded in their actual social interactions rather than simulated annotations. By annotating implicit emotions from reader comments and synchronously capturing implicit multi-label propagation intents, EmoIntenNet provides a unique resource to model the intrinsic triadic coupling among implicit emotion, implicit intent, and explicit behavior, grounded in social context. We establish a comprehensive evaluation protocol, testing state-of-the-art methods, including large language models (LLMs) with advanced reasoning enhancement. Our findings reveal significant shortcomings in existing approaches when handling this multifaceted, user-aware task. EmoIntenNet offers a foundational resource and clear direction for future research in socially-intelligent NLP, pushing models towards a more unified understanding of emotion on social media.</abstract>
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%0 Conference Proceedings
%T Beyond the Retweet: A Large-Scale Dataset of Reader Emotion, Propagation Intent, and Behavior
%A Zheng, Yujin
%A Liao, Jian
%A Wang, Suge
%A Zheng, JianXing
%A Li, Deyu
%A Chen, Qian
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F zheng-etal-2026-beyond-retweet
%X Current emotion analysis in social media is predominantly author-centric, failing to capture the subjective nature of emotional responses across diverse readers. This paradigm overlooks the crucial link between individual perception, implicit propagation intent, propagation behavior, and the underlying social network. To bridge this gap, we introduce EmoIntenNet, a novel bilingual (English and Chinese) large-scale benchmark that uniquely integrates six critical dimensions for social perception: author-created content, actual readers’ emotional feedback (derived from their comments), implicit propagation intent, explicit propagation behavior, user attributes, and the social relationship graph. This benchmark advances the shift towards more granular, reader-centric analysis, where different readers’ emotional responses to the same content are grounded in their actual social interactions rather than simulated annotations. By annotating implicit emotions from reader comments and synchronously capturing implicit multi-label propagation intents, EmoIntenNet provides a unique resource to model the intrinsic triadic coupling among implicit emotion, implicit intent, and explicit behavior, grounded in social context. We establish a comprehensive evaluation protocol, testing state-of-the-art methods, including large language models (LLMs) with advanced reasoning enhancement. Our findings reveal significant shortcomings in existing approaches when handling this multifaceted, user-aware task. EmoIntenNet offers a foundational resource and clear direction for future research in socially-intelligent NLP, pushing models towards a more unified understanding of emotion on social media.
%U https://aclanthology.org/2026.emnlp-main.841/
%P 19506-19542
Markdown (Informal)
[Beyond the Retweet: A Large-Scale Dataset of Reader Emotion, Propagation Intent, and Behavior](https://aclanthology.org/2026.emnlp-main.841/) (Zheng et al., EMNLP 2026)
ACL
- Yujin Zheng, Jian Liao, Suge Wang, JianXing Zheng, Deyu Li, and Qian Chen. 2026. Beyond the Retweet: A Large-Scale Dataset of Reader Emotion, Propagation Intent, and Behavior. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 19506–19542, Budapest, Hungary. Association for Computational Linguistics.