中文学术论文的微信提及数据特征分析研究

Comprehensive Analysis of Data Characteristics in Chinese Academic Papers Mentioned on WeChat

目前中文替代计量数据的开发和应用研究仍旧较少,本研究面向中国自主知识体系建设,以中文学术论文的微信提及数据作为研究对象,探究其不同层次的数据特征分布,为中文替代计量指标的开发及研究提供基础。基于2020年1月中国知网收录的所有中文学术论文的微信提及数据,通过统计分析和数据可视化,揭示微信提及指标在表现形式、及时性、论文层次、来源层次、学科层次的分布特征。主要研究结论如下:①微信提及的表现形式以非参考文献为主,占比高达60.80%;②微信提及的及时性为56.56%,微信提及中文学术论文早于出版日期的原因主要是学者在会议、讲座中分享阶段性成果;③提及中文学术论文的微信公众号账户主体多元且分布较为离散,核心期刊论文更容易受到微信文章的提及,期刊微信账户对本刊发表论文有较为明显的自提及行为;④提及学术论文使得微信文章更具专业性和权威性,对微信文章的传播效应具有促进作用,并且微信文章的提及也有助于学术论文和科研成果的传播分享,提升其社会影响力。

Research on the development and application of Chinese altmetric data is limited.This study aims to contribute to the construction of China’s independent knowledge system by investigating the distribution characteristics of numerical values associated with Chinese academic papers mentioned on WeChat.This study presents the distribution characteristics of WeChat mention indicators of all the Chinese academic papers mentioned on WeChat and included in the China National Knowledge Infrastructure(CNKI)in January 2021.The presentation form and timeliness were analyzed along with paper,source,and discipline levels using statistical analysis and data visualization.The main finding of this study is that nonreferenced literature accounts for 60.80% of WeChat mentions.Second,the timeliness of WeChat mentions was 56.56%,mainly because scholars shared their research findings at conferences and lectures.Third,the Chinese academic papers mentioned on WeChat official accounts are diverse and scattered.Papers published in core journals are more likely to be mentioned,and the WeChat accounts of journals exhibit noticeable self-mention behavior.Fourth,mentions of academic papers can enhance the professionalism and authority of WeChat articles,promoting their dissemination along with research results,thereby increasing their social impact.