基于弹幕分析的在线直播平台用户理解
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国家自然科学基金(61962038);广西多源信息挖掘与安全重点实验室开放基金(MIMS17-01);福建省自然科学基金(2017J01497);福建省教育厅K类科技项目(JK2016007)


Time-Sync Comments Analyzation for Understanding Subscribers to Live Streaming Services
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    摘要:

    弹幕评论是网络直播平台与用户交互的主要方式之一,借助弹幕行为的分析可以更有效地实现对网络直播平台的用户理解.通过采集和利用3大热门直播平台(斗鱼、熊猫与战旗)的弹幕相关数据,本文以假设验证的方式从用户属性与用户行为两个角度对在线直播平台用户进行分析与理解,并建立基于用户行为特征时间序列的用户活跃模型对用户互动活跃度进行量化评估.研究表明,平台在线人数具有周期性变化的时间规律,观众地域具有沿海发达城市集中分布的空间取向,所提出的用户活跃模型能够对网络直播平台用户的行为活跃趋势做出合理的预测分析.

    Abstract:

    It is an important communication way for webcast video watchers to produce and consume time-sync comments, which can be beneficial to understand the webcast video users. Based on data related to time-sync comment collected from 3 hot live streaming platforms (Douyu, Panda and Zhanqi), a hypothesis testing based method is proposed to analyze webcast video watchers from user attribute and user behavior, a user activity model is constructed based on user behavior feature time series analysis. Research results show that, the number of live streaming platform online users has obvious characteristics of periodic changes, source of live streaming platform online users tends to be distributed in inshore developed cities, and the proposed user activity model can effectively predict activity of users in live streaming platforms.

    参考文献
    [1] Geerts D, Vaishnavi I, Mekuria R, et al. Are we in sync? Synchronization requirements for watching online video together. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. New York, NY, USA. 2011. 311.
    [2] Liu LL, Suh A, Wagner C. Investigating communal interactive video viewing experiences online. In:Kurosu M, ed. Human-Computer Interaction. Cham:Springer, 2016.
    [3] 李君贤. 网络直播中弹幕语言暴力机制的形成与消解. 西部学刊, 2016, (10):59-60
    [4] He M, Ge Y, Chen EH, et al. Exploring the emerging type of comment for online videos:Danmu. ACM Transactions on the Web, 2018, 12(1):1-33.[doi:10.1145/3180440
    [5] Chen XP, Chen JY, Ma L, et al. Fine-grained video attractiveness prediction using multimodal deep learning on a large real-world dataset. International World Wide Web Conferences Steering Committee. Geneva, Switzerland. 2018. 671-678.
    [6] Chung CT, Hsiung HK, Wei CK, et al. Towards personalized video summarization using synchronized comments and Probabilistic Latent Semantic Analysis. 2014 IEEE 3rd Global Conference on Consumer Electronics. Tokyo, Japan. 2014. 414-415.
    [7] Shen Y, Chan H, Hung I. Let the comments fly:The effects of flying commentary presentation on consumer judgment. International Journal of Systems Science, 2014, 12(12):1469-1475
    [8] Schultes P, Dorner V, Lehner F. Leave a comment! An in-depth analysis of user comments on YouTube. 11th International Conference on Wirtschaftsinformatik. Leipzig, Germany. 2013. 659-673.
    [9] Hu M, Zhang ML, Luo NA. Understanding participation on video sharing communities:The role of self-construal and community interactivity. Computers in Human Behavior, 2016, 62:105-115.[doi:10.1016/j.chb.2016.03.077
    [10] 邓扬, 张晨曦, 李江峰. 基于弹幕情感分析的视频片段推荐模型. 计算机应用, 2017, 37(4):1065-1070, 1134.[doi:10.11772/j.issn.1001-9081.2017.04.1065
    [11] 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. 现代图书情报技术, 2015, 31(11):82-90
    [12] Wu B, Zhong EH, Tan B, et al. Crowdsourced time-sync video tagging using temporal and personalized topic modeling. Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, NY, USA. 2014. 721-730.
    [13] Xu LL, Zhang C. Bridging video content and comments:Synchronized video description with temporal summarization of crowdsourced time-sync comments. AAAI Publications, Thirty-First AAAI Conference on Artificial Intelligence. 2017. 1611-1617.
    [14] 肖云鹏. 在线社会网络用户行为模型与应用算法研究[博士学位论文]. 北京:北京邮电大学, 2013.
    [15] 樊鹏翼, 王晖, 姜志宏, 等. 微博网络测量研究. 计算机研究与发展, 2012, 49(4):691-699
    [16] Thelwall M, Sud P, Vis F. Commenting on youtube videos:From guatemalan rock to el big bang. Journal of the American Society for Information Science and Technology, 2012, 63(3):616-629.[doi:10.1002/asi.21679
    [17] 中国互联网络信息中心. 中国互联网络发展状况统计报告. 2018.
    [18] 吴慧, 张绍武, 林鸿飞. 微博社交网络的用户影响力评价方法. 中文信息学报, 2017, 31(4):184-190.[doi:10.3969/j.issn.1003-0077.2017.04.026
    [19] 张效尉, 余云霞, 王伟. 社交网络群中用户活跃度分析与预测. 西南师范大学学报(自然科学版), 2018, 43(12):115-121
    [20] 王锦坤, 姜元春, 孙见山, 等. 考虑用户活跃度和项目流行度的基于项目最近邻的协同过滤算法. 计算机科学, 2016, 43(12):158-162.[doi:10.11896/j.issn.1002-137X.2016.12.028
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黄发良,谢国庆,陈子炜.基于弹幕分析的在线直播平台用户理解.计算机系统应用,2020,29(2):250-256

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  • 收稿日期:2019-06-17
  • 最后修改日期:2019-07-12
  • 在线发布日期: 2020-01-16
  • 出版日期: 2020-02-15
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