Visualization communication mode and path optimization of data news in the context of big data

被引：0

作者：

Zhang H. ^{[1
]}

机构：

[1] School of Journalism and Communication, Communication University of China, Nanjing Jiangsu, Nanjing

来源：

Applied Mathematics and Nonlinear Sciences | 2024年 / 9卷 / 01期

关键词：

Big data; Data journalism; IE-Page Rank; Information entities; Visualization;

D O I：

10.2478/amns.2023.2.00140

中图分类号：

学科分类号：

摘要：

With the development of big data technology, not only driving the development of the social economy but also the news media industry is developing in the direction of integration and innovation, and promoting the dissemination of news through data value factors is the focus of current research. This paper takes data news as the research object, takes the framework theory as the entry point, and mainly studies the data news production dilemma and its optimization path. Firstly, the data news information is classified by entity extraction, and the weights between the entity information are calculated to establish the association. Secondly, the IE-Page Rank algorithm is proposed to get the IER value of each information entity by iterative calculation, which is used to identify its importance and quantitatively get the importance ranking of all information entities. Finally, the basic framework of data news visualization is constructed, and the applicable visualization optimization dissemination path is given in the case. The research results show that compared with the traditional media news dissemination model, the improved data visualization dissemination model increases efficiency by 32.3%, timeliness by 18.9%, user satisfaction by 21.1%, and effectively increases the reading volume and dissemination paths by 17.2% of users. The improved data news visualization dissemination model proposed in this paper improves the professionalization of data analysis, enhances the interactivity and visualization of data news works, and provides guidance for disseminating data news. © 2023 Hezhen Zhang, published by Sciendo.

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