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題名 Ritual in Live Streaming: Reasoning Interaction through Multimodal Large Language Model
作者 向倩儀
Hsiang, Chien-Yi;Yeh, Chao-Chuan
貢獻者 資管系
日期 2025-12
上傳時間 2026-01-19
摘要 Game live-streaming has become a popular entertainment form. Its real-time interaction boosts viewer engagement and sponsorship behavior. However, previous research has rarely explored how interactions gradually develop, boost through emotional resonance, and lead to specific outcomes over time. This study introduces a multimodal analytical framework that combines signals from live streaming and uses a multimodal large language model with Chain-of-Thought (CoT) reasoning to build a step-by-step inference process. This process uncovers the emotional and group dynamics behind interactions. The framework includes four tasks: detecting shared moods among viewers, identifying collective effervescence, evaluating the resulting interaction outcomes, and estimating their likelihood of continuing in future streams. Together, these elements offer a more transparent and well-structured overview of live-streaming interactions, highlighting the practical applications of multimodal large language models in analyzing intricate, cross-modal interaction patterns.
關聯 Proceedings of the International Conference on Information Systems, The International Conference on Information Systems (ICIS)
資料類型 conference
dc.contributor 資管系
dc.creator (作者) 向倩儀
dc.creator (作者) Hsiang, Chien-Yi;Yeh, Chao-Chuan
dc.date (日期) 2025-12
dc.date.accessioned 2026-01-19-
dc.date.available 2026-01-19-
dc.date.issued (上傳時間) 2026-01-19-
dc.identifier.uri (URI) https://ah.lib.nccu.edu.tw/item?item_id=180637-
dc.description.abstract (摘要) Game live-streaming has become a popular entertainment form. Its real-time interaction boosts viewer engagement and sponsorship behavior. However, previous research has rarely explored how interactions gradually develop, boost through emotional resonance, and lead to specific outcomes over time. This study introduces a multimodal analytical framework that combines signals from live streaming and uses a multimodal large language model with Chain-of-Thought (CoT) reasoning to build a step-by-step inference process. This process uncovers the emotional and group dynamics behind interactions. The framework includes four tasks: detecting shared moods among viewers, identifying collective effervescence, evaluating the resulting interaction outcomes, and estimating their likelihood of continuing in future streams. Together, these elements offer a more transparent and well-structured overview of live-streaming interactions, highlighting the practical applications of multimodal large language models in analyzing intricate, cross-modal interaction patterns.
dc.format.extent 127 bytes-
dc.format.mimetype text/html-
dc.relation (關聯) Proceedings of the International Conference on Information Systems, The International Conference on Information Systems (ICIS)
dc.title (題名) Ritual in Live Streaming: Reasoning Interaction through Multimodal Large Language Model
dc.type (資料類型) conference