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.
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.
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.
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.
Funding: This study was supported by Humanities and Social Sciences Youth Foundation, Ministry of Education of the People’s Republic of China (Grant No. 21YJC740085).
Zhu, H. and Liu, X. (2023), "Do linguistic features of research article titles affect received online attention? A corpus-based analysis", Library Hi Tech, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/LHT-01-2023-0022
Emerald Publishing Limited
Copyright © 2023, Emerald Publishing Limited