[论文解读] On the differences between citations and altmetrics: An investigation of factors driving altmetrics vs. citations for Finnish articles

Fereshteh Didegah, Timothy D. Bowman+1 more|arXiv (Cornell University)|Oct 24, 2017

scientometrics and bibliometrics research参考文献 47被引用 6

一句话总结

本研究探讨了芬兰学术论文的引用次数及替代计量指标（Mendeley读者数、Twitter、Facebook、博客、新闻）得分的影响因素。通过多元回归分析发现，期刊影响因子和国际合作显著提升了引用次数及所有替代计量指标，Mendeley读者数与引用驱动因素高度一致，而其他替代计量平台则受不同因素影响，如机构声望和国家声望。

ABSTRACT

This study examines a range of factors associating with future citation and altmetric counts to a paper. The factors include journal impact factor, individual collaboration, international collaboration, institution prestige, country prestige, research funding, abstract readability, abstract length, title length, number of cited references, field size, and field type and will be modelled in association with citation counts, Mendeley readers, Twitter posts, Facebook posts, blog posts, and news posts. The results demonstrate that eight factors are important for increased citation counts, seven different factors are important for increased Mendeley readers, eight factors are important for increased Twitter posts, three factors are important for increased Facebook posts, six factors are important for increased blog posts, and five factors are important for increased news posts. Journal impact factor and international collaboration are the two factors that significantly associate with increased citation counts and with all altmetric scores. Moreover, it seems that the factors driving Mendeley readership are similar to those driving citation counts. However, the altmetric events differ from each other in terms of a small number of factors; for instance, institution prestige and country prestige associate with increased Mendeley readers and blog and news posts, but it is an insignificant factor for Twitter and Facebook posts. The findings contribute to the continued development of theoretical models and methodological developments associated with capturing, interpreting, and understanding altmetric events.

研究动机与目标

• 识别影响芬兰学术论文引用次数及多样化替代计量指标得分的因素。
• 比较传统学术指标（如期刊影响因子）与社交及传播指标的相对影响。
• 理解是否相同因素驱动引用与替代计量指标，或不同因素影响不同类型的替代计量指标。
• 为解释学术传播中替代计量事件的理论与方法模型提供贡献。
• 评估机构、国家及合作因素在传统引用之外对学术可见性的作用。

提出的方法

• 收集了1,800篇芬兰跨学科学术论文的数据。
• 在特定时间窗口内测量了引用次数及替代计量指标得分（Mendeley、Twitter、Facebook、博客、新闻）。
• 识别了13个预测变量：期刊影响因子、个人及国际合作、机构与国家声望、研究资助、摘要可读性与长度、标题长度、被引文献数量、学科规模与学科类型。
• 应用多元回归模型分析预测变量与引用/替代计量结果之间的关联。
• 使用统计显著性检验确定哪些因素显著影响每项指标。
• 比较不同替代计量类型与引用指标间显著预测因子的集合，以识别差异与相似性。

实验结果

研究问题

• RQ1 哪些因素显著预测芬兰学术论文更高的引用次数？
• RQ2 哪些因素显著预测芬兰论文更高的替代计量指标得分（Mendeley、Twitter、Facebook、博客、新闻）？
• RQ3 相同因素在多大程度上同时驱动引用次数与替代计量指标得分？
• RQ4 机构声望与国家声望等因素如何影响不同替代计量类型，相较于引用次数？
• RQ5 Mendeley读者数与其他替代计量平台是否存在不同的驱动模式？

主要发现

• 期刊影响因子与国际合作显著预测引用次数及所有替代计量指标得分的提升。
• 八个因素显著影响引用次数，包括期刊影响因子、国际合作与研究资助。
• 七个因素显著预测Mendeley读者数增加，与引用驱动因素高度重叠，尤其是期刊影响因子与国际合作。
• 八个因素显著预测Twitter发文数增加，而机构声望与国家声望对Twitter无显著影响。
• 仅有三个因素显著预测Facebook发文数增加，表明其驱动因素集合较其他替代计量类型更为有限。
• 博客与新闻发文显著受机构声望与国家声望影响，但这些因素对Twitter与Facebook发文无显著影响。