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短视频平台用户交互行为的差异性 被引量:1

Differences in User Interaction Behaviour on Short Video Platforms
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摘要 研究不同短视频平台用户交互行为的差异,对平台导向和用户选择有着重要价值。文章基于趣味区隔理论对不同短视频平台在信息传播过程中各维度的差异性进行研究,以抖音、快手、b站3个平台“徐州八孩母亲”事件相关视频为例,对拉斯韦尔5W传播模式中传播者、内容、接收者、反馈效果4个维度的差异性进行假设检验,结合主题模型挖掘标题文本与评论文本的主题倾向,并借助布尔迪厄趣味区隔理论对差异性进行阐释与分析。研究发现:不同短视频平台的差异性源于用户间形成的趣味区隔,用户在短视频传播的交互中不断强化其价值观边界,同时平台逐渐形成趣味共同体;短视频平台用户交互行为差异反映出不同的阶层需求和价值观差异,而平台又通过自身调性构建来迎合和强化其特性。 Studying the differences in user interaction behaviour between different short video platforms has important implications for platform orientation and user choice.Based on the theory of taste differentiation(or,taste-based distinction),this paper examines the differences between different short video platforms in the process of information dissemination.Taking the videos related to the incident of“Mother of eight children in Xuzhou”on three platforms(Douyin,Kuaishou and Bilibili)as an example,the paper tests the hypotheses on the differences between the four dimensions of Laswell’s5W model of communication:communicator,message,audience,and effect,and explores the thematic tendency of the video titles and comments using the thematic model.Bourdieu's theory of taste differentiation is used to explain and analyse the differences.The study finds that the differences between different short video platforms stem from the interest differentiation formed among users,and that users constantly reinforce their value boundaries through the interactions during the dissemination of short videos,while the platforms gradually turn into different interest-based communities;behavioural differences in user interactions on short video platforms signal different demands and values of different social classes,and the platforms,in turn,cater to and reinforce their characteristics by building their own styles.
作者 赵月华 盛予欣 ZHAO Yuehua;SHENG Yuxin
出处 《图书馆论坛》 CSSCI 北大核心 2024年第2期114-122,共9页 Library Tribune
基金 教育部人文社会科学基金项目“多源网络环境下虚假医疗健康信息智能识别研究”(项目编号:20YJC870014)研究成果。
关键词 短视频平台 信息传播 用户交互行为 趣味区隔 文本挖掘 short video platform information dissemination user interaction behaviour taste-based distinc-tion text mining
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