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数据分析与知识发现  2019, Vol. 3 Issue (2): 33-42    DOI: 10.11925/infotech.2096-3467.2018.0552
  研究论文 本期目录 | 过刊浏览 | 高级检索 |
微信群会话话题强度计算及演化分析*
汪鸿沁泠(),巴志超,李纲
武汉大学信息资源研究中心 武汉 430072
Conversational Topic Intensity Calculation and Evolution Analysis of WeChat Group
Hongqinling Wang(),Zhichao Ba,Gang Li
Center for Studies of Information Resources, Wuhan University, Wuhan 430072, China
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摘要 

【目的】通过探究实际微信群内部的话题结构及演化特征, 对微信用户交互行为特点及信息传播规律进行探讨。【方法】以三个典型性微信群对话样本作为研究对象, 引入语言学中的会话分析理论, 分析微信群会话语言现象及特点, 设计基于成员活跃度、交流强度及话轮密度的话题强度计算模型, 并进一步探究不同类型的微信群中会话的话题结构特征及演化规律。【结果】微信群会话与日常会话的语言现象具有同一性及差异性, 将话轮纳入话题强度计算模型较消息条数有明显优势, 不同类型的微信群享有各自的话题演化规律。【局限】微信群类型丰富性可以进一步增加。【结论】本研究有利于把握话题在微信群中的发展规律, 对网络舆情监控及灾害防治有重要意义。

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汪鸿沁泠
巴志超
李纲
关键词 微信群会话分析话题演化话题强度    
Abstract

[Objective] This paper aims to study the characteristics of WeChat user interaction and information dissemination by exploring the topic structure and evolution characteristics within the actual WeChat group. [Methods] Taking three typical WeChat group conversation samples as research objects, we introduce the conversation analysis theory in linguistics, and analyze the phenomenon and characteristics of the WeChat group conversation, and design the topic intensity calculation model based on the activeness of membership, the intensity of communication and turn density, and further explore the topic structure characteristics and evolution rules in different types of WeChat groups. [Results] The linguistic phenomena of WeChat group conversations and daily conversations have the sameness and difference. The inclusion of the turn-taking into the topic intensity calculation model has obvious advantages over the number of messages. Different types of WeChat groups respectively own their topic evolution rules. [Limitations] The richness of WeChat group type can be increased. [Conclusions] This study is conducive to grasp the development law of topics in the WeChat group, and is of great significance to the monitoring of Internet public opinion and disaster prevention.

Key wordsWeChat Group    Conversation Analysis    Topic Evolution    Topic Intensity
收稿日期: 2018-05-17     
基金资助:*本文系国家自然科学基金青年项目“突发公共卫生事件社交媒体信息主题演化与影响力建模”(项目编号: 71603189)和国家社会科学基金重大项目“大数据时代计算社会科学的产生、现状与发展前景研究”(项目编号: 16ZDA086)的研究成果之一
引用本文:   
汪鸿沁泠,巴志超,李纲. 微信群会话话题强度计算及演化分析*[J]. 数据分析与知识发现, 2019, 3(2): 33-42.
Hongqinling Wang,Zhichao Ba,Gang Li. Conversational Topic Intensity Calculation and Evolution Analysis of WeChat Group. Data Analysis and Knowledge Discovery, DOI:10.11925/infotech.2096-3467.2018.0552.
链接本文:  
http://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.2096-3467.2018.0552
[1] 2017年微信用户数据报告:8.89亿月活跃用户1 000万公众号[EB/OL]. [2018-03-20].
[1] (WeChat Users’ Data Report of 2017: 8.89 Million Users Monthly Active in 10 Million Public Accounts[EB/OL]. [2018-03-20]. )
[2] 刘姝辰. 人人网发展困境探究[D]. 济南: 山东师范大学, 2015.
[2] (Liu Shuchen.To Explore the Plight of the Web of Renren[D]. Ji’nan: Shandong Normal University, 2015.)
[3] 朱艳. 基于微信平台结构的人际交往研究[D]. 南京: 南京大学, 2015.
[3] (Zhu Yan.Research on Interpersonal Communication Base on the Structure of the WeChat[D]. Nanjing: Nanjing University, 2015.)
[4] Salton G, Wong A, Yang C S.A Vector Space Model for Automatic Indexing[J]. Communications of the ACM, 1974, 18(11): 613-620.
[5] 张秋霞, 谷平. 文档主题提取算法VSM改进与应用[J]. 情报杂志, 2007, 26(12): 17-19.
[5] (Zhang Qiuxia, Gu Ping.Improvement of Keyword Weight Algorithm in Vector Space Model[J]. Journal of Intelligence, 2007, 26(12): 17-19.)
[6] Bosch A, Zisserman A.Scene Classification via pLSA[A]// Lecture Notes in Computer Science[M]. Springer Berlin Heidelberg, 2006: 517-530.
[7] 黄卫东, 陈凌云, 吴美蓉. 网络舆情话题情感演化研究[J].情报杂志, 2014, 33(1): 102-107.
[7] (Huang Weidong, Chen Lingyun, Wu Meirong.Research on Sentiment Evaluation of Online Public Opinion Topic[J]. Journal of Intelligence, 2014, 33(1): 102-107.)
[8] Blei M D, Ng Y A, Jordan I M.Latent Dirichlet Allocation[J]. Journal of Machine Learning Research, 2003, 3: 993-1022.
[9] 林萍, 黄卫东. 基于LDA模型的网络突发事件话题演化路径研究[J]. 情报科学, 2014, 32(10): 20-23.
[9] (Lin Ping, Huang Weidong.Topic Evolution Analysis of Internet Emergency Based on LDA Model[J]. Information Science, 2014, 32(10): 20-23.)
[10] 余传明, 张小青, 陈雷. 基于LDA模型的评论热点挖掘: 原理与实现[J]. 情报理论与实践, 2010, 33(5): 103-106.
[10] (Yu Chuanming, Zhang Xiaoqing, Chen Lei.Principles and Implementation of Hotspot Based on LDA Models[J]. Information Studies: Theory & Application, 2010, 33(5): 103-106.)
[11] 廖君华, 孙克迎, 钟丽霞. 一种基于时序主题模型的网络热点话题演化分析系统[J]. 图书情报工作, 2013, 57(5): 96-102.
[11] (Liao Junhua, Sun Keying, Zhong Lixia.Study on a Hot Topics Analysis System Based on Time Sliced Topic Model[J]. Library and Information Service, 2013, 57(5): 96-102.)
[12] 陈卓群. 基于共词网络的社交媒体话题演化分析[J]. 情报科学, 2015, 33(1): 120-125.
[12] (Chen Zhuoqun.Analysis of Topic Evolution in Social Media Based on Co-word Network[J]. Information Science, 2015, 33(1): 120-125.)
[13] Grifiths T L, Steyvers M.Finding Scientific Topics[J]. Proceeding of the National Academy of Science, 2004, 101(S1): 5228-5235.
[14] 贺亮, 李芳. 基于话题模型的科技文献话题发现和趋势分析[J]. 中文信息学报, 2012, 26(2): 109-115.
[14] (He Liang, Li Fang.Topic Discovery and Trend Analysis in Scientific Literature Based on Topic Model[J]. Journal of Chinese Information Processing, 2012, 26(2): 109-115.)
[15] 徐佳俊, 杨飏, 姚天昉, 等. 基于LDA模型的论坛热点话题识别和追踪[J]. 中文信息学报, 2016, 30(1): 43-49.
[15] (Xu Jiajun, Yang Yang, Yao Tianfang, et al.LDA Based Hot Topic Detection and Tracking for the Forum[J]. Journal of Chinese Information Processing, 2016, 30(1): 43-49.)
[16] 乔善增. 基于种子文档和统计模型的话题演化研究[D]. 济南: 山东大学, 2014.
[16] (Qiao Shanzeng.Research on Topic Evolution with Seed Document & Statistical Model[D]. Ji’nan: Shandong University, 2014.)
[17] 彭利斌. 微博热点话题发现与话题演化的研究[D]. 桂林: 桂林电子科技大学, 2014.
[17] (Peng Libin.Research on Hot Topic Detection and Topic Evolution on Microblog[D]. Gulin: Guilin University of Electronic Technology, 2014.)
[18] 杨星. 基于LDA的话题获取与演化研究[D]. 郑州: 河南工业大学, 2013.
[18] (Yang Xing.The Research on Topic Access and Evolution with LDA[D]. Zhengzhou: Henan University of Technology, 2013.)
[19] 孙孟孟. 基于名词短语提取与词条权重分析的话题提取算法研究[D]. 杭州: 浙江大学, 2014.
[19] (Sun Mengmeng.Topic Extraction Algorithm Based on NP-Chunking and Phrase Weight Calculation[D]. Hangzhou: Zhejiang University, 2014.)
[20] Sacks H, Schegloff E A, Jefforson G.Simplest Systematics for the Organization of Turn-talking for Conversation[J]. Language, 1974, 50: 696-735.
[21] Schegloff E A.Sequence Organization in Interaction: A Primer in Conversation Analysis (Volume 1)[M]. Cambridge University Press, 2007.
[22] Schegloff E A, Jefferson G, Sacks H.The Preference for Self-Correction in the Organization of Repair in Conversation[J]. Language, 1997, 53: 361-384.
[23] Barske T G.Co-Constructing Social Roles in German Business Meetings: A Conversation Analytic Study[D]. University of Illinois, 1997.
[24] Fearson D S.Social Enaction: How Talk -in-interaction Constitutes Social Organizations[D]. Los Angeles: University of California, 2005.
[25] Curl T S.The Phonetics of Sequential Organization:An Investigation of Lexical Repetition in Other-initiated Repair Sequences in American English[D]. University of Colorado, 2002.
[26] Lazaraton A L.A Conversation Analysis of Structure and Interaction in Language Interview[D]. Los Angeles: University of California, 1991.
[27] 郭恩华, 张德禄. 基于会话分析的多模态交际研究探索——论序列分析在CA多模态交际研究中的应用[J]. 北京第二外国语学院学报, 2017, 39(3): 31-45.
[27] (Guo Enhua, Zhang Delu.Reflections on CA-based Multimodal Interaction Research: The Application of Sequential Analysis in Multimodel Interaction Research[J]. Journal of Beijing International Studies University, 2017, 39(3): 31-45.)
[28] 沈燕. BBS主题帖的会话分析研究[D]. 武汉: 华中师范大学, 2014.
[28] (Shen Yan.Conversation Analysis Study of BBS Topic Posting[D]. Wuhan: Central China Normal University, 2014.)
[29] 马费成, 夏永红. 网络信息的生命周期实证研究[J].情报理论与实践, 2009, 32(6): 1-7.
[29] (Ma Feicheng, Xia Yonghong.Empirical Research on Network Information Life Cycle[J]. Information Studies: Theory & Application, 2009, 32(6): 1-7.)
[30] 刘晓娟, 王昊贤, 张爱芸. 微博信息生命周期研究[J]. 图书情报工作, 2014, 58(1): 72-78.
[30] (Liu Xiaojuan, Wang Haoxian, Zhang Aiyun.Research on Lifecycle of Microblog[J]. Library and Information Service, 2014, 58(1): 72-78.)
[31] 王巍. 会话话题分析[D]. 长春: 吉林大学, 2006.
[31] (Wang Wei.Analysis of Discourse Topic[D]. Changchun: Jilin University, 2006.)
[32] 张雪. 对话体语篇分析[D]. 上海: 华东师范大学, 2006.
[32] (Zhang Xue.Analysis of Conversational Discourse[D]. Shanghai: East China Normal University,2006.)
[33] 杜海燕, 叶光辉. 社交博客用户分层与话题演化研究——以MetaFilter Music版块为例[J]. 信息资源管理学报, 2015, 5(4): 39-46.
[33] (Du Haiyan, Ye Guanghui.Research on User Classification and Topic Evolution in Social Blog: Empirical Analysis Based on Music Section in MetaFilter Dataset[J]. Journal of Information Resources Management, 2015, 5(4): 39-46.)
[34] 李纲, 海岚, 陈璟浩. 突发自然灾害事件网络媒体报道的周期特征分析——以地震和台风灾害为例[J]. 信息资源管理学报, 2015, 5(3): 18-24.
[34] (Li Gang, Hai Lan, Chen Jinghao.A Survival Analysis of Periodic Characteristics of China’s Emergent Natural Disaster Event Reported by Network Media: A Case Study on Earthquake and Typhoon Disaster[J]. Journal of Information Resources Management, 2015, 5(3): 18-24.)
[35] 王芳, 翟羽佳. 微信群社会结构及其演化: 基于文本挖掘的案例分析[J]. 情报学报, 2016, 35(6): 617-629.
[35] (Wang Fang, Zhai Yujia.Social Structure and Evolvement of WeChat Groups: A Case Study Based on Text Mining[J]. Journal of the Society for Scientific and Technical Information, 2016, 35(6): 617-629.)
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