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
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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。
[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".
[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".
引文
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[21] 张秋, 杨玲, 王曼. 高校图书馆微信公众平台服务发展现状及对策[J]. 图书馆建设,2014(2):61-65,69.
[22] 万慕晨, 欧亮. 基于微信公众平台的高校图书馆阅读推广效果实证研究[J]. 图书情报工作,2015,59(22):72-78.
[23] 李武, 吴月华, 刘宇. 青少年社会化阅读动机与行为之关系研究——以上海市初高中生微信阅读为例[J]. 图书情报工作,2014,58(23):61-68.
[24] 清博大数据. 清博大数据平台[EB/OL]. [2017-12-19]. http://www.gsdata.cn/.
[25] 清博指数. 微信传播指数WCI(V 13.0)[EB/OL]. [2017-12-19]. http://www. gsdata.cn/site/usage.
[26] 王磊, 吕鹏辉, 张仁琼. 高校图书馆微信信息计量分析[J]. 图书情报知识,2017 (5):49-58,88.
[2] 张晓梅. 浅议阅读推广中的服务创新[J]. 图书情报,2017,61(1):29-30.
[3] 何韵, 何兰满. 从传统阅读与数字阅读的二元关系论全民阅读推广策略——以日本为例[J]. 图书馆, 2015(7):34-38.
[4] ANTONIO J, ALONSO J, GOMEZ R, et al. Social reading: platforms, applications, clouds and tags[M]. Oxford: chandos pulishing ltd, 2013.
[5] 孙一钢, 张炜, 谢强, 等. 国家数字图书馆的数字阅读服务与推广[J]. 图书馆杂志,2016(10):4-8.
[6] 张晓青, 张植禾, 相春艳. 基于Web2.0的知识传播研究[J]. 现代传播(中国传媒大学学报),2010(4):123-126.
[7] 姜志芳. 信息用户需求及信息用户行为研究综述[J]. 图书馆论坛,2006(4):284-286,278.
[8] 杨婧. 基于社交媒体的图书馆阅读推广研究[J]. 图书馆研究,2017(5):57-60.
[9] LI W, WU Y. Adolescents’ social reading: motivation, behaviour, and their relationship[J]. Electronic library, 2017,35(2):246-262.
[10]张云,茆意宏. 高校学生社会化阅读行为调查分析[J]. 国家图书馆学刊,2016,25(2):77-87.
[11] 刘静. 我国高校图书馆认证用户微博调查分析——以新浪微博为平台[J]. 图书馆学研究,2012(1):90-95.
[12] KIBUGI S. The use of social media in the dissemination of information in selected public and private university libraries in Kenya[J]. Innovation: journal of appropriate librarianship and information work in Southern Africa, 2013(47):100-120.
[13] ZINOVIEV D, DUONG V, ZHANG H. A game theoretical approach to modeling information dissemination in social networks[EB/OL]. [2018-10-10]. https://arxiv.org/pdf/1006.5493v1.pdf.
[14] YANG D, CHOW T W S, ZHONG L, et al. True and fake information spreading over the Facebook[J]. Physica a-statistical mechanics & its applications, 2018,505: 984-994.
[15] CHANCE R, LESESNE T. Rethinking reading promotion[J]. Teacher librarian, 2012,39(5):26-32.
[16] DIKE V W, EKE H N, BABARINNDE E T. Social media and reading among secondary school students in Enugu State[J]. Mousaion, 2013(31):60-81.
[17] SAMPENE A B, AFORO A A. Impact of social media on academic reading: a study at Kwame Nkrumah University of Science and Technology, Kumasi, Ghana [J]. Asian journal of humanities and social studies, 2014,2(1):92-99.
[18] LI W, WU Y, LIU Y. Study on relationship between adolescents’ social reading motivation and their behaviors: a case study of middle school students’ reading via Wechat in Shanghai [J]. Library & information service, 2014,40(6):115-128.
[19] SUN L, XIE J. An exploration of reading promotion and readers’ advisory at Chinese public libraries[J]. Reference & user services quarterly, 2016, 56(2):96.
[20] 苑世芬. 高校图书馆新媒体阅读推广策略研究[J]. 现代情报,2013,33(1):74-77,81.
[21] 张秋, 杨玲, 王曼. 高校图书馆微信公众平台服务发展现状及对策[J]. 图书馆建设,2014(2):61-65,69.
[22] 万慕晨, 欧亮. 基于微信公众平台的高校图书馆阅读推广效果实证研究[J]. 图书情报工作,2015,59(22):72-78.
[23] 李武, 吴月华, 刘宇. 青少年社会化阅读动机与行为之关系研究——以上海市初高中生微信阅读为例[J]. 图书情报工作,2014,58(23):61-68.
[24] 清博大数据. 清博大数据平台[EB/OL]. [2017-12-19]. http://www.gsdata.cn/.
[25] 清博指数. 微信传播指数WCI(V 13.0)[EB/OL]. [2017-12-19]. http://www. gsdata.cn/site/usage.
[26] 王磊, 吕鹏辉, 张仁琼. 高校图书馆微信信息计量分析[J]. 图书情报知识,2017 (5):49-58,88.