Subjects: Library Science，Information Science >> Library Science submitted time 2023-07-26 Cooperative journals: 《图书情报工作》
Abstract： [Purpose/significance] As the WeChat public platform promotes the intelligent level of library information dissemination and enhances the efficiency of library services, it has been widely used by Chinese libraries as an important self-media platform. In this context, this paper attempts to compare the WeChat public platform of university libraries and public libraries by the data of WeChat public platform.[Method/process]Firstly, the external and internal description data pushed in WeChat public platform was collected via the big data platform of social media and web crawler technology. After cleaning, this data was stored in the Mongo DB database. Secondly, the WeChat communication Index (WCI) was calculated through corresponding algorithm. At the time, the latent dirichlet allocation (LDA) was applied to carry out the topic classification research of texts. Finally, the conceptand quantitative method of information accepting and adopting index(IA&AI) was proposed, and the text feature was extracted by setting of IA&AI threshold.[Result/conclusion] There is strongly positive correlation between the number of readings and the number of praises; theoverall influence of the public platform of university libraries is slightly lower than that of public libraries, but the polarization of WCI exists in both kinds of libraries; after comparative analysis, the similarities and differences of topic classification and text features in high(or low) IA&AI's text are found.Finally, based on the research conclusions, some suggestions were put forward to operate the WeChat public platform of the two kinds of libraries.
Subjects: Library Science，Information Science >> Library Science submitted time 2023-07-26 Cooperative journals: 《图书情报工作》
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".
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