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图书情报工作 ›› 2019, Vol. 63 ›› Issue (2): 12-23.DOI: 10.13266/j.issn.0252-3116.2019.02.002

• 专题:数据驱动研究新范式 • 上一篇    下一篇

社会化阅读环境下阅读内容计量研究——以阅读推广类微信公众号推送文章为例

王磊1, 江仔玲2, 刘宇婷3   

  1. 1. 合肥工业大学图书馆 合肥 230009;
    2. 合肥工业大学仪器科学与光电工程学院 合肥 230009;
    3. 苏州大学纺织与服装工程学院 苏州 215021
  • 收稿日期:2018-07-03 修回日期:2018-10-12 出版日期:2019-01-20 发布日期:2019-01-20
  • 作者简介:王磊(ORCID:0000-0002-8000-7223),信息咨询与学科服务部主管,副研究馆员,博士,E-mail:wangl@hfut.edu.cn;江仔玲(ORCID:0000-0002-3843-8740),本科生;刘宇婷(ORCID:0000-0003-2597-3944),本科生。
  • 基金资助:
    本文系中央高校基本科研业务费专项资金资助项目"扎根社交媒体大数据的社会化阅读探究"（项目编号：JS2018HGXJ0042）研究成果之一。

Informetrics Study of the Reading Contents in Social Reading Environment: Take the Articles Published by Wechat Public Platform of Reading Promotion for Example

Wang Lei1, Jiang Ziling2, Liu Yuting3   

  1. 1. Hefei University of Technology Library, Hefei 230009;
    2. School of Instrument Science and Opto-electric Engineering, Hefei University of Technology, Hefei 230009;
    3. Textile and Clothing of Engineering, Soochow University, Suzhou 215021
  • Received:2018-07-03 Revised:2018-10-12 Online:2019-01-20 Published:2019-01-20

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摘要： [目的/意义]随着社交媒体大数据平台的构建、大数据挖掘、分析技术及数据库技术的不断演进，社交媒体中各种数据变得可获取、可利用。据此，开展了基于社交媒体数据的社会化阅读内容计量研究。[方法/过程]首先，利用大数据平台及文本挖掘技术对阅读推广类微信公众号基本信息和公众号推送文章内容进行采集。其次，对采集的数据进行统计，得到了各微信公众号在分类、地域、认证上的分布，以及各微信公众号推送文章数、阅读数、平均阅读数、点赞数、平均点赞数，并通过运用信息计量学相关理论及技术，利用自编脚本得到了单篇推文传播指数（Single Tweets Communication Index，STCI）、标题高频关键词共现矩阵、基于推文重复次数的微信公众号耦合矩阵。同时，提出最高STCI微信推文发布延时指数（Publish Delay Index of Highest STCI Wechat Ariticle，HSPDI）算法来考察推文重复发布次数对推文传播效果的影响。最后，采用人工研读高STCI推文的方法对其内容特征进行归纳。[结果/结论]阅读推广类微信公众号推送文章的平均阅读数与平均点赞数存在一定的正相关关系；许多微信公众号之间存在严重的推送相同文章现象；文章是否是微信公众号首次推送对其传播没有明显影响。进一步，对高STCI推文内容分析后得出高STCI推文特征主要包括：满足读者的好奇心、满足读者的自我表达需要和满足"屏阅读"时代特征的写作法则。

关键词: 社会化阅读, 社交媒体, 微信公众号, 信息计量

Abstract: [Purpose/significance] With the development of the social media big data platform, evolutionary of the big data and database technology, the various data are becoming available and accessible in web 2.0. In this context, the paper tries to explore the feasibility of informetrics study of reading contents based on the big data of social media.[Method/process] The paper applies the big data platform and text mining technology to collect the basic information of WeChat public account and the contents of the published articles. Then, the collected data are counted. The distribution of WeChat public account on classification, geographical and certification, as well as the number of published articles, reading number, average reading number, thumbs up number and average thumb up number up of WeChat public account are obtained. In addition, the Single Tweets Communication Index (STCI), the high frequency keywords in the title co-occurrence matrix and coupling matrix based on repetitions of WeChat public account articles are proposed by using the theories and techniques of the informetrics. At the same time, the paper puts forward the Publish Delay Index of Highest STCI Article (HSPDI) algorithm to investigate the effect of repeating publish times of articles on the communication effect of the articles. Finally, the paper summarizes the content characteristics of articles through artificial reading of high STCI articles.[Result/conclusion] There is a positive correlation between the average reading number and the average thumb up number in Wechat public accounts. Many wechat public accounts have the same phenomenon of publishing the same article. Whether the first time to publish the article has no obvious effect on its STCI. Furthermore, after analyzing the content of high STCI tweets, the paper puts forward three main features of the high STCI articles, including satisfaction of the readers' curiosity, the meeting of the reader's self-expression needs, and the compliance of the writing principle of "screen reading".

Key words: social reading, social media, WeChat Public account, informetrics

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