Citation and Similarity in Academic Texts: Colombian Engineering Case

Abstract

This article provides the results of a citation determinants model for a set of academic engineering texts from Colombia. The model establishes the determinants of the probability that a text receives at least one citation through the relationship among previous citations, journal characteristics, the author and the text. Through a similarity matrix constructed by Latent Semantic Analysis (LSA), a similarity variable has been constructed to capture the fact that the texts have similar titles, abstracts and keywords to the most cited texts. The results show: i) joint significance of the variables selected to characterize the text; ii) direct relationship of the citation with similarity of keywords, published in an IEEE journal, research article, more than one author; and authored by at least one foreign author; and iii) inverse relationship between the probability of citation with the similarity of abstracts, published in 2016 or 2017, and published in a Colombian journal.

Keywords

• latent semantic analysis
• text similarity
• citation determinants
• bibliometrics