Altmetrics: Factor Analysis for Assessing the Popularity of Research Articles on Twitter

• Published in: Journal of information science theory and practice Vol. 7; no. 4; pp. 33 - 44
• Main Authors: Pandian, Nandhini Devi Soundara, Na, Jin-Cheon, Veeramachaneni, Bhargavi, Boothaladinni, Rashmi Vishwanath
• Format: Journal Article
• Language: Korean
• Published: 2019
• Subjects: factor analysis; twitter metric; negative binomial regression; altmetrics
• ISSN: 2287-9099

Altmetrics measure the frequency of references about an article on social media platforms, like Twitter. This paper studies a variety of factors that affect the popularity of articles (i.e., the number of article mentions) in the field of psychology on Twitter. Firstly, in this study, we classify Twitter users mentioning research articles as academic versus non-academic users and experts versus non-experts, using a machine learning approach. Then we build a negative binomial regression model with the number of Twitter mentions of an article as a dependant variable, and nine Twitter related factors (the number of followers, number of friends, number of status, number of lists, number of favourites, number of retweets, number of likes, ratio of academic users, and ratio of expert users) and seven article related factors (the number of authors, title length, abstract length, abstract readability, number of institutions, citation count, and availability of research funding) as independent variables. From our findings, if a research article is mentioned by Twitter users with a greater number of friends, status, favourites, and lists, by tweets with a large number of retweets and likes, and largely by Twitter users with academic and expertise knowledge on the field of psychology, the article gains more Twitter mentions. In addition, articles with a greater number of authors, title length, abstract length, and citation count, and articles with research funding get more attention from Twitter users.