情报研究

中文学术期刊论文的微博提及行为及Altmetrics融合评价研究

  • 余佳潞 ,
  • 岑汪慧 ,
  • 韩毅
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  • 西南大学商贸学院 重庆 402460
余佳潞,硕士研究生,设计研究方案,数据整理、初稿撰写及修改;岑汪慧,硕士研究生,数据搜集,数据分析

收稿日期: 2024-01-18

  修回日期: 2024-04-08

  网络出版日期: 2024-10-17

基金资助

本文系国家社会科学基金项目“开放科学视域下中文Altmetrics数据的生成机制与可用性评估研究”(项目编号:22BTQ059)研究成果之一。

A Study on Weibo Mentions and Altmetrics-Based Evaluation of Chinese Academic Journal Articles

  • Yu Jialu ,
  • Cen Wanghui ,
  • Han Yi
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  • Business College of Southwest University, Chongqing 402460

Received date: 2024-01-18

  Revised date: 2024-04-08

  Online published: 2024-10-17

Supported by

This work is supported by the National Social Science Fund of China project titled “Researches on the Generation Mechanism and Usability Evaluation of Chinese Altmetrics Data from the Perspective of Open Science” (Grant No. 22BTQ059).

摘要

[目的/意义] 探讨中文社交媒体平台提及中文学术期刊论文行为的特点,并据此构建适应中文学术生态情景的更细粒度Altmetrics融合评价指标,为全面、深入评价中文学术期刊论文质量拓展新思路。[方法/过程] 以新浪微博提及学术期刊论文为研究对象,采用内容分析法从提及方式、提及动机与提及情感3个维度探索中文社交媒体用户的论文提及行为特征,综合提及方式、提及情感、提及动机构建Altmetrics融合评价指标FI,利用层次分析法确定指标计算权重,通过实证研究验证融合指标的适用性。[结果/结论] 中文社交媒体平台提及行为生成的Altmetrics数据对评价中文学术论文影响力具有较高的应用价值;综合提及方式、提及情感、提及动机的多维度FI指标的评价更加全面且区分度更强,一定程度上能消除单纯社交媒体提及次数指标的片面性。

本文引用格式

余佳潞 , 岑汪慧 , 韩毅 . 中文学术期刊论文的微博提及行为及Altmetrics融合评价研究[J]. 图书情报工作, 2024 , 68(19) : 90 -104 . DOI: 10.13266/j.issn.0252-3116.2024.19.008

Abstract

[Purpose/Significance] It is highly important to explore the mention behavior characteristics of Chinese academic journal articles on Chinese social media platforms, and construct more fine-grained Altmetrics integrated evaluation indicators to adapt to the Chinese academic ecological scenario. This will facilitate a comprehensive and in-depth evaluation for Chinese academic journal articles. [Method/Process] Taking the academic journal articles mentioned on Weibo as the research object, this paper used content analysis to explore the characteristics of Chinese social media users’ mentioning behavior of academic journal articles from three dimensions: mentioning method, mentioning motivation, and mentioning sentiment. It constructed Altmetrics integrated evaluation indicators by combining these dimensions, determining the calculation weight of these indicators using analytic hierarchy process, and verifying the applicability of the integrated indicators through empirical research. [Result/Conclusion] Altmetrics data generated by mentions on Chinese social media platforms have high application value in evaluating the influence of Chinese academic papers. Evaluating these articles using multidimensional indicators that integrate mentioning method, mentioning sentiment, and mentioning motivation provides a more comprehensive and discriminating assessment, which can eliminate the one-sidedness of the pure social media mention frequency indicator to a certain extent.

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