Reaction视频中用户弹幕信息交互行为的情感反应生成机理研究  

Exploring the Generation Mechanism of Affective Responses of User Danmaku Commenting Behavior in Reaction Videos

作　　者：叶许婕 赵宇翔[1] 张妍[2] 李金昊 Preben Hansen Ye Xujie;Zhao Yuxiang;Zhang Yan;Li Jinhao;Preben Hansen(School of Economics&Management,Nanjing University of Science&Technology,Nanjing,210094;Laboratory of Data Intelligence and Interdisciplinary Innovation,Nanjing University,Nanjing,210023;College of Business,City University of Hong Kong,Hong Kong,999077,China;Department of Computer and Systems Sciences,Stockholm University,Stockholm,SE-10691)

机构地区：[1]南京理工大学经济管理学院,南京210094 [2]南京大学数据智能与交叉创新实验室,南京210023 [3]香港城市大学商学院,中国香港999077 [4]斯德哥尔摩大学计算机与系统科学系,斯德哥尔摩SE-10691

出　　处：《信息资源管理学报》2024年第2期104-120,共17页Journal of Information Resources Management

基　　金：国家自然科学基金面上项目“公共文化服务领域开放数据的价值共创机制及实现模式研究”(72074112)的研究成果之一。

摘　　要：深入挖掘Reaction视频中弹幕信息交互行为的情感反应机理有助于理解用户弹幕创作背后的情感生成原因及情感变化过程。本文基于情感反应模型,利用定向内容分析法对哔哩哔哩网站中11个热门视频的弹幕信息资源、视频内容以及reactor反应情况展开编码研究,构建了Reaction视频中用户弹幕信息交互行为的情感反应生成机理模型。研究发现,Reaction视频弹幕信息交互行为中的情感反应生成机理总体上遵循“信息刺激-情感反应”的路径,信息刺激有时会独立唤醒情绪或特定情感态度,有时也会通过唤醒特定情感态度进而影响情绪或内化情感态度的生成。该模型有助于提升情感反应理论在计算机协助交流中的情境化探索,也将为社交媒体中用户与信息交互提供优化建议。Investigating the generation mechanism of affective response of danmaku commenting behavior in reaction videos can provide valuable insights into the reasons for affective generations and the process of affective change.This paper takes reaction videos of the Bilibili video website as examples.We conduct coding using the directed content analysis method by selecting the danmaku resources,video content,and reactor responses of 11 popular videos in different camps as samples.Based on the Affective Response Model(ARM),this paper builds a theoretical framework of the generation mechanism of affective responses of user danmaku commenting behavior in reaction videos.The results suggest that affective responses of user danmaku commenting behavior in reaction videos generally follows the path of"information cues-affective response",that is,information cues can arouse emotions or particular affective responses autonomously,and they can also affect the generation of emotions or learned affective responses by arousing particular affective responses.The proposed framework helps to improve the contextualized exploration of ARM theory in computer-mediated communication and will also provide practical implications for optimizing user-information interaction in social media.

关 键 词：Reaction视频 弹幕 用户信息交互 情感反应模型 定向内容分析 

分 类 号：G251[文化科学—图书馆学]