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数据分析与知识发现  2022, Vol. 6 Issue (7): 56-69     https://doi.org/10.11925/infotech.2096-3467.2021.1449
  研究论文 本期目录 | 过刊浏览 | 高级检索 |
在线社区用户画像及自我呈现主题挖掘——以网易云音乐社区为例*
吴江1,2,3,刘涛3,刘洋1,3()
1武汉大学信息资源研究中心 武汉 430072
2武汉大学电子商务研究与发展中心 武汉 430072
3武汉大学信息管理学院 武汉 430072
Mining Online User Profiles and Self-Presentations: Case Study of NetEase Music Community
Wu Jiang1,2,3,Liu Tao3,Liu Yang1,3()
1Center for Studies of Information Resources, Wuhan University, Wuhan 430072, China
2Center for E-commerce Research and Development, Wuhan University, Wuhan 430072, China
3School of Information Management, Wuhan University, Wuhan 430072, China
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摘要 

目的】以网易云音乐社区为研究对象,分析在线社区中用户自我呈现的内容特征、演化规律、群体差异及自我呈现对社区认可的影响等问题。【方法】从资历和参与度两个维度构建用户画像,通过BERT方法进行短文本评论聚类,挖掘自我呈现的内容主题,利用余弦相似度等方法分析用户自我呈现内容主题的演化规律和群体差异,采用协方差分析用户自我呈现内容主题对社区认可度的影响。【结果】用户自我呈现的内容主题分为8类;“听后感”主题占比逐年降低,“回忆往事”等主题呈上升趋势;“寻求互动”等主题在“放松”等曲风下占比要高于其他曲风;除“寻求互动”主题外,各主题在不同时间点上占比一致;“回忆往事”等主题下高资历用户的余弦相似度高于低资历用户;持续参与用户的余弦相似度高于边缘参与者;用户自我呈现内容主题对其社区认可度的影响在10%的置信度水平下显著。【局限】 未针对其他类型的在线社区进行更深入的研究。【结论】用户自我呈现的内容主题以“回忆往事”为主,会受到曲风等因素的影响,内容主题随社区发展呈现泛化趋势且不同用户群体之间有明显差异,在线社区中用户对自我呈现内容主题有一定的偏好。

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吴江
刘涛
刘洋
关键词 自我呈现用户画像BERT主题聚类群体差异在线社区    
Abstract

[Objective] This paper explores patterns, evolutionary laws, group differences and influences on community recognition of online users’ self-presentation topics. [Methods] Firstly, we identified online users of NetEase music community and constructed their profiles from the perspectives of qualification and participation. Then, we adopted the BERT model to cluster users’ short comments, and identified their self-presentation topics. Third, we utilized cosine similarity to analyze the evolution of topics and group differences. Finally, we used covariance to analyze the impacts of self-presentation topics on community recognition. [Results] There are eight self-presentation topics, while the proportion of “reviews” decreased and “recollection” increased. “Interaction”topics were more popular in “relax” style than in others. The proportion of each topic at different time was almost the same. Under the themes of “recollection”, the cosine similarity value of quality users was higher than those of other users. The cosine similarity of continuous participants was higher than those of the inactive participants. The impact of users’ self-presentation topics on their community recognition was significant at the 0.1 level. [Limitations] More research is needed to examine users of other online communities. [Conclusions] “Recollection” is the most popular one among users’ self-presentation topics, which are affected by styles and time. There was a diversity trend for the topics with the development of the community, as well as obvious differences among user groups.

Key wordsSelf-Presentation    User Profile    BERT Topic Clustering    Group Differences    Online Community
收稿日期: 2021-12-24      出版日期: 2022-08-24
ZTFLH:  F49 G203  
基金资助:*国家教育部哲学社会科学研究重大课题攻关项目的研究成果之一(20JZD024)
通讯作者: 刘洋,ORCID:0000-0002-9410-1755     E-mail: yang.liu27@whu.edu.cn
引用本文:   
吴江, 刘涛, 刘洋. 在线社区用户画像及自我呈现主题挖掘——以网易云音乐社区为例*[J]. 数据分析与知识发现, 2022, 6(7): 56-69.
Wu Jiang, Liu Tao, Liu Yang. Mining Online User Profiles and Self-Presentations: Case Study of NetEase Music Community. Data Analysis and Knowledge Discovery, 2022, 6(7): 56-69.
链接本文:  
https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.2096-3467.2021.1449      或      https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/Y2022/V6/I7/56
Fig.1  在线社区中用户自我呈现行为的研究框架
Fig.2  不同曲风下的评论数量
Fig.3  不同长度的评论数量
属性 字段 英文名称
用户资历 注册时间 days
粉丝数 fans
歌单被订阅数 subscribe
用户付费 vip
用户付费等级数 viplevel
用户参与度 用户创建歌单数 playlist
用户创建动态数 event
关注数 follows
Table 1  基础属性定义
Fig.4  用户画像构建方法
days
fans 0.020 8
subscribe 0.015 3
viplevel 0.244 4
Table 2  注册天数与粉丝数、歌单被订阅数及付费等级的Pearson系数
数值 fans(比例) subscribe(比例)
[0,5) 46.13% 87.21%
[0,10) 67.70% 92.77%
[0,20) 84.19% 95.70%
[0,50) 94.35% 97.62%
[0,100) 97.10% 98.37%
Table 3  用户粉丝数、歌单被订阅数的数据分布
Fig.5  BERT及LDA聚类结果对比
主题识别 含义 主题 占比 关键词
回忆往事 与用户过往经历有关的故事,如爱情、亲情、学生时代的经历等 Topic 1 3.95% 男孩、女孩、喜欢、朋友、分手
Topic 4 4.40% 小学、学生、同学、学习、音乐
Topic 8 7.26% 对不起、我爱你、放弃、别人
Topic 10 8.69% 感觉、也许、时间、再也无法
Topic 14 5.93% 高三、学校、三年、想起
Topic 16 2.81% 初中、女孩、学生、暑假、脑海
Topic 25 5.10% 变好、姑娘、不够、埋怨、真心
人生感悟 用户抒发的人生感想与体会 Topic 3 4.86% 希望、世界、孤独、放弃、发现
Topic 24 7.43% 思念、淡化、每个人、永远
留言 用户借歌曲评论区留言祈福、设定目标等 Topic 2 3.67% 高考、一年、加油、时间、大学
Topic 6 2.47% 努力、鼓励、考生、可能、转折
Topic 13 7.01% 想要、决定、做梦、目标、向前
歌曲信息 与歌曲相关的信息,如歌手、歌曲推荐等 Topic 18 0.19% 几首歌、谢安琪、欢乐颂、老樊
Topic 20 1.74% 重温、风格、韵律、原曲、吉他
Topic 26 0.67% 声音、纯音、佳作、创作、理解
听后感 用户对歌曲的评价及歌曲给自身带来的感受 Topic 7 5.47% 听到、好听、一首歌、循环
Topic 17 4.55% 好难过、挥之不去、歌单、那句
Topic 23 1.84% 小众、不敢、平静、温馨、怀念
寻求互动 用户表露互动的行为,如求赞等 Topic 15 0.04% 上午好、中午好、祝老板、点赞
Topic 21 0.10% 网恋么、有没有、有人么、陪你
天马行空 用户天马行空的想法与评论,一般与歌曲无太多的关系 Topic 9 0.32% 周游、摇滚、大佬、战袍、兰姨
Topic 11 4.36% 嘿嘿、豪任、摇起来、呵呵
Topic 12 0.03% 难熬、寡呱、打卡、指挥官
Topic 19 8.38% 抵挡、返回、红蜡烛、提醒
Topic 22 2.47% 苏联、红军、多边形、国民
当前状态 用户当下所处的环境或状态等 Topic 5 6.26% 晚上、生日、降温、加班、现在
Table 4  自我呈现内容主题
Fig.6  不同年份下用户的自我呈现内容主题分布
Fig.7  不同曲风下的用户自我呈现内容主题分布
Fig.8  不同曲风下的用户自我呈现内容主题占比
Fig.9  不同时间下的用户自我呈现内容主题分布
Fig.10  不同时间下的用户自我呈现内容主题占比
主题 L1 L2 L3 L4
Topic 18 1.58% 2.09% 2.51% 3.92%
Topic 1 3.23% 4.24% 5.14% 8.09%
Topic 4 3.68% 4.87% 5.87% 9.15%
Topic 8 3.57% 4.74% 5.72% 9.06%
Topic 10 3.51% 4.66% 5.58% 8.74%
Topic 14 3.53% 4.70% 5.68% 8.84%
Topic 16 1.77% 2.36% 2.85% 4.40%
Topic 25 3.74% 4.93% 5.94% 9.32%
Topic 2 1.39% 1.85% 2.29% 3.50%
Topic 6 2.11% 2.81% 3.39% 5.24%
Topic 13 3.70% 4.90% 5.91% 9.25%
Topic 3 3.58% 4.77% 5.71% 8.97%
Topic 24 3.67% 4.89% 5.87% 9.17%
Topic 9 1.11% 1.45% 1.75% 2.77%
Topic 11 3.18% 4.19% 5.06% 8.02%
Topic 22 2.79% 3.70% 4.47% 6.92%
Topic 21 1.67% 2.19% 2.66% 4.17%
Table 5  用户资历与自我呈现内容主题余弦相似度
主题 边缘参与者 初始参与者 持续参与者
Topic 5 2.68% 2.41% 16.02%
Topic 23 2.69% 2.40% 15.55%
Topic 26 2.67% 2.39% 15.54%
Topic 1 2.67% 2.40% 15.63%
Topic 16 1.48% 1.32% 8.59%
Topic 2 1.16% 1.02% 6.84%
Topic 6 1.76% 1.57% 10.22%
Topic 9 0.91% 0.81% 5.36%
Topic 19 2.48% 2.24% 15.04%
Topic 15 0.38% 0.34% 2.26%
Topic 21 1.38% 1.24% 8.07%
Table 6  用户参与度与自我呈现内容主题余弦相似度
Partial SS df MS F Prob>F
Model 7.22×107 136 531 045 4.9 0.00***
topic 1.41×106 7 201 809 1.9 0.07*
style 3.53×106 11 320 468 2.9 0.00***
year 2.73×107 6 4 543 720 41.8 0.00***
comment_num 2.29×105 1 228 765 2.1 0.15
year×topic 7.13×106 38 187 523 1.7 0.00***
style×topic 9.28×106 73 127 170 1.2 0.15
Residual 1.17×109 10 703 108 820
Table 7  获赞量的协方差分析结果
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