Do linguistic features of research article titles affect received online attention? A corpus-based analysis

IF 3.4 3区 管理学 0 INFORMATION SCIENCE & LIBRARY SCIENCE Library Hi Tech Pub Date : 2023-06-29 DOI:10.1108/lht-01-2023-0022
Haoran Zhu, Xueying Liu
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Abstract

Purpose Scientific impact is traditionally assessed with citation-based metrics. Recently, altmetric indices have been introduced to measure scientific impact both within academia and among the general public. However, little research has investigated the association between the linguistic features of research article titles and received online attention. To address this issue, the authors examined in the present study the relationship between a series of title features and altmetric attention scores.Design/methodology/approach The data included 8,658 titles of Science articles. The authors extracted six features from the title corpus (i.e. mean word length, lexical sophistication, lexical density, title length, syntactic dependency length and sentiment score). The authors performed Spearman’s rank analyses to analyze the correlations between these features and online impact. The authors then conducted a stepwise backward multiple regression to identify predictors for the articles' online impact.Findings The correlation analyses revealed weak but significant correlations between all six title features and the altmetric attention scores. The regression analysis showed that four linguistic features of titles (mean word length, lexical sophistication, title length and sentiment score) have modest predictive effects on the online impact of research articles.Originality/value In the internet era with the widespread use of social media and online platforms, it is becoming increasingly important for researchers to adapt to the changing context of research evaluation. This study identifies several linguistic features that deserve scholars’ attention in the writing of article titles. It also has practical implications for academic administrators and pedagogical implications for instructors of academic writing courses.
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研究文章标题的语言特征会影响在线关注度吗?基于语料库的分析
科学影响传统上是用基于引用的指标来评估的。最近,已经引入了替代指标来衡量学术界和公众中的科学影响。然而,很少有研究调查研究论文标题的语言特征与网络关注之间的关系。为了解决这一问题,作者在本研究中考察了一系列标题特征与替代注意分数之间的关系。数据包括8,658篇科学文章的标题。作者从标题语料库中提取了六个特征(即平均词长、词汇复杂度、词汇密度、标题长度、句法依赖长度和情感得分)。作者运用斯皮尔曼的排名分析来分析这些特征与网络影响力之间的相关性。然后,作者进行了逐步反向多元回归,以确定文章在线影响力的预测因素。结果:相关分析显示,6个标题特征与非均衡性注意得分之间存在较弱但显著的相关性。回归分析表明,标题的四个语言特征(平均词长、词汇复杂度、标题长度和情感得分)对研究文章的网络影响有适度的预测作用。在社交媒体和在线平台广泛使用的互联网时代,研究人员适应不断变化的研究评估环境变得越来越重要。本研究确定了文章标题写作中值得学者注意的几个语言特征。它对学术管理人员和学术写作课程的教师也有实际意义。
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来源期刊
Library Hi Tech
Library Hi Tech INFORMATION SCIENCE & LIBRARY SCIENCE-
CiteScore
8.30
自引率
44.10%
发文量
97
期刊介绍: ■Integrated library systems ■Networking ■Strategic planning ■Policy implementation across entire institutions ■Security ■Automation systems ■The role of consortia ■Resource access initiatives ■Architecture and technology ■Electronic publishing ■Library technology in specific countries ■User perspectives on technology ■How technology can help disabled library users ■Library-related web sites
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