| 基于弹幕分析的在线直播平台用户理解 |
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| 引用本文： | 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[J].计算机系统应用,2020,29(2):250-256. |
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| 作者姓名： | 黄发良 谢国庆 陈子炜 |
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| 作者单位： | 广西师范大学 广西多源信息挖掘与安全重点实验室, 桂林 541004;福建省公共服务大数据挖掘与应用工程技术研究中心, 福州 350117;福建师范大学 数学与信息学院, 福州 350117;福建师范大学 数学与信息学院, 福州 350117 |
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| 基金项目： | 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） |
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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.
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| 关 键 词： | 用户理解 弹幕分析 在线直播 社会媒体 |
| 收稿时间： | 2019/6/17 0:00:00 |
| 修稿时间： | 2019/7/12 0:00:00 |
Time-Sync Comments Analyzation for Understanding Subscribers to Live Streaming Services |
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| HUANG Fa-Liang,XIE Guo-Qing and CHEN Zi-Wei.Time-Sync Comments Analyzation for Understanding Subscribers to Live Streaming Services[J].Computer Systems& Applications,2020,29(2):250-256. |
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| Authors: | HUANG Fa-Liang XIE Guo-Qing and CHEN Zi-Wei |
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| Affiliation: | Guangxi Key Lab of Multi-Source Information Mining & Security, Guangxi Normal University, Guilin 541004, China,Fujian Engineering Research Center of Public Service Big Data Mining and Application, Fuzhou 350117, China;College of Mathematics and Informatics, Fujian Normal University, Fuzhou 350117, China and College of Mathematics and Informatics, Fujian Normal University, Fuzhou 350117, China |
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| 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. |
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| Keywords: | user understanding time-sync comments analyzation live webcast social media |
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