Research on User Interaction Experience in Participatory Websites Based on the Bullet Screen

Yang Qian, Diao Yajing, Li Jiaming, Ge Shilun

Knowledge Management Forum ›› 2022, Vol. 7 ›› Issue (4) : 417-430.

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Knowledge Management Forum ›› 2022, Vol. 7 ›› Issue (4) : 417-430. DOI: 10.13266/j.issn.2095-5472.2022.035

Research on User Interaction Experience in Participatory Websites Based on the Bullet Screen

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Abstract

[Purpose/Significance] The online interaction of participatory websites improves the efficiency of information transmission and communication, and improves users' perception and experience. Through the study of bullet screen under participatory website, it complements the relevant research on user experience theory, and provides practical guidance for short video disseminators and relevant participatory websites. [Method/Process] This paper selected Bilibili, a typical participatory website, as the research object, took the short video bullet screen data and aggregated hotness indicator data of Bilibili as the research samples to study its user interaction experience, used python to crawl bullet screen popularity and content information of short videos, and studied its users’ aggregated hotness indicators and user generated contents of participatory websites through correlation analysis and text emotion analysis. [Result/Conclusion] The results show that there is a correlation between the number of bullet screen, the number of comments and the number of the video playback of participatory websites; Real time bullet screen interaction enhances the audience's cognitive sharing, emotional experience and onlooker experience.

Key words

participatory website / user experience / bullet screen interaction / text analysis

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Yang Qian , Diao Yajing , Li Jiaming , et al. Research on User Interaction Experience in Participatory Websites Based on the Bullet Screen[J]. Knowledge Management Forum. 2022, 7(4): 417-430 https://doi.org/10.13266/j.issn.2095-5472.2022.035

References

[1]
冯钰茹, 邓小昭. 弹幕视频网站用户弹幕评论行为的影响因素研究——以Bilibili弹幕视频网站为例[J]. 图书情报工作, 2021, 65(17): 110-116.
[2]
罗汉洋, 李智妮, 林旭东,等. 网络口碑影响机制:信任的中介和性别及涉入度的调节[J]. 系统管理学报, 2019, 28(3): 401-414,428.
[3]
金晓玲, 周中允, 尹梦杰,等. 在线用户点赞与评论行为的产生机理差异研究——以医疗健康类企业微信公众号为例[J]. 管理科学学报, 2021, 24(4): 54-68.
[4]
汪旭晖, 王东明. 平台卖家生成内容对于消费者信任的影响研究——平台企业生成内容的交互效应[J/OL]. 南开管理评论.[2022-06-23].https://t.cnki.net/kcms/detail?v=UfSR4g5EZlgk4VSRdh8IgxbXrgMO35ewi7fzmPUw4tE6pNdF-4nU4uJ00QbPFyxSprBNDc96-YwRHeNURs9o79NMpN0LS-MAuwdoAAgEFRuDvLhZkuiH1A==&uniplatform=NZKPT.
[5]
刘蕾, 于春玲, 赵平. 图文信息对消费者互动行为及品牌关系的影响[J]. 管理科学, 2018, 31(1): 90-100.
[6]
MATHIAS S, BERNARD D C, CHLOE L. When form deviates from the norm: attitudes towards old and new vernacular features and their impact on the perceived credibility and usefulness of Facebook consumer reviews [J].Language sciences.[2022-06-23]. https://www.sciencedirect.com/science/article/pii/S0388000121000607.
[7]
叶笛, 林伟沣. 虚拟品牌社区用户参与价值共创行为的驱动因素[J]. 中国流通经济, 2021, 35(10): 93-105.
[8]
毕达天, 贯君, 李洁. 基于信息运动视角的虚拟社区互动机理研究[J]. 图书情报工作, 2015, 59(20): 112-118,148.
[9]
姚山季, 王富家, 刘德文. 内容型虚拟社区中的用户互动和融入:身份认同的中介效应[J]. 商业经济与管理, 2018(2): 64-78.
[10]
李光明, 蔡旺春, 黄永春. 基于消费者价值视角的购物网站特性对电子忠诚度的影响[J]. 软科学, 2015, 29(7): 98-101.
[11]
蒋璐珺, 巩淼森, 蒋晓. 心流视角下网络购物平台交互体验设计研究[J]. 包装工程, 2018, 39(2): 214-218.
[12]
李春发, 邹雅玲, 王雪红,等. WEEE回收网站交互性对消费者回收行为的影响——消费者交易感知的中介作用[J]. 科技管理研究, 2015, 35(3): 209-214.
[13]
范思. 参与式网站中用户体验与交互行为研究 [D]. 武汉: 华中科技大学, 2018.
[14]
刘春茂, 米国伟. 基于Web2.0的网络用户群“社会性”行为的系统分析[J]. 图书情报工作, 2010, 54(20): 32-35,85.
[15]
岳丽姣, 时利, 刘法勇. 一种面向高速公路自动驾驶的人机交互方案设计[J]. 汽车实用技术, 2021, 46(16): 30-32.
[16]
王娴雅. 应用计算机辅助技术的舰船导航界面交互设计[J]. 舰船科学技术, 2021, 43(14): 148-150.
[17]
吕昊, 张成元. 基于投影仪摄像机系统的人机交互关键技术研究[J]. 科学技术创新, 2020(10): 74-76.
[18]
姜伟, 陈毅文, 张玉婷. 参照群体对线上申办信用卡行为意愿的影响——感知控制的中介作用、感知风险的调节作用[J]. 人类工效学, 2021, 27(2): 57-65.
[19]
WANG Y. Humor and camera view on mobile short-form video apps influence user experience and technology-adoption intent, an example of TikTok ( DouYin ) [J]. Computers in human behavior, 2020, 110:106373.
[20]
孙林辉, 韩贝贝, 张伟. 基于眼动实验的英语学习类手机APP界面设计评价[J]. 人类工效学, 2021, 27(2): 1-8.
[21]
王晰巍, 郑国梦, 王铎,等. 虚拟现实阅读用户交互体验评价指标构建及实证研究[J]. 图书情报工作, 2020, 64(16): 54-66.
[22]
梁赛, 田佳佳, 刁建超,等. 基于三维度理论的游客在线评分情感异质性及影响因素研究[J/OL]. 南开管理评论.[2022-06-23].https://kns.cnki.net/kcms/detail/detail.aspx?dbcode=CAPJ&dbname=CAPJLAST&filename=LKGP20210809000&uniplatform=NZKPT&v=0gy_6gslfyRIjymWb0GeqLh1NbXFWUaZROs0vfEdC6vpF14hy4jg3Pks_V3SRNxc.
[23]
LI Z, LI R,JIN G H. Sentiment analysis of Danmaku videos based on naive bayes and sentiment dictionary[J]. IEEE access, 2020, 8: 75073-75084.
[24]
BAI Q, WEI K, ZHOU J, et al. Entity-level sentiment prediction in Danmaku video interaction[J]. The journal of supercomputing volume, 2021, 77(9): 9474-9493.
[25]
ZHAO J F, LI Y. Influence of emotional expression in online comments on consumers' perception[J/OL]. Journal of ambient intelligence and humanized computing.[2022-06-23].https://doi.org/10.1007/s12652-021-03472-7.
[26]
RODRIGUEZ T F E, DIAZ M R. The influence of outsourcing activities on the perception of service quality. an empirical study based on online reviews by hotel customers[J]. Journal of hospitality and tourism technology, 2021, 12(4):689-711.
[27]
MEIJERINK J, SCHOENMAKERS E. Why are online reviews in the sharing economy skewed toward positive ratings? linking customer perceptions of service quality to leaving a review of an Airbnb stay [J]. Journal of tourism futures, 2020,7(1):5-19.
[28]
MAHLKE S. Factors influencing the experience of website usage[C]//Human factors in computing systems. New York: ACM, 2002:846-847.
[29]
NORMAN D, MILLER J, HENDERSON A. What you see, some of what's in the future, and how we go about doing it[C]//Human factors in computing systems. New York: ACM, 1995:155.
[30]
ROTO V. User experience building blocks[C]//The 2nd COST294-MAUSE international open workshop. New York: ACM, 2006:124-128.
[31]
ROSE S, CLARK M, SAMOUEL P, et al. Online customer experience in e-retailing: an empirical model of antecedents and outcomes[J]. Journal of retailing, 2012, 88(2): 308-322.
[32]
陈忆金, 曹树金, 陈桂鸿. 网络舆情意见挖掘:用户评论情感倾向分析研究[J]. 图书情报知识, 2013(6): 90-96.
[33]
洪巍, 李敏. 文本情感分析方法研究综述[J]. 计算机工程与科学, 2019, 41(4): 750-757.
[34]
LIM S, CHA S Y, PARK C, et al. Getting closer and experiencing together: antecedents and consequences of psychological distance in social media-enhanced real-time streaming video[J]. Computers in human behavior, 2012, 28(4): 1365-1378.
[35]
郑飏飏, 徐健, 肖卓. 情感分析及可视化方法在网络视频弹幕数据分析中的应用[J]. 现代图书情报技术, 2015(11): 82-90.

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