Linear Regression Analysis of Title Word Count and Article Time Cited using R

There is a common idea, that title variables like article title length would influence article citations. The aim of the present study was to investigating possible relationship between size of article title and number of article citations by minimizing scientometric variable biases. A dataset containing ~100,000 virological literatures was obtained from Web of Science InCite^TM database from 1997 to 2016. Variables, Title size (TWC), Year (YoP), Source (JS), and Publisher were selected. In addition number of times article is cited ‘Time Cited’ (TC) was retrieved from Web of Science InCite^TM. Linear regression analysis was performed between variables and TC using R for a possible prediction model for TC. Result has shown a robust standard error corrected linear regression with only 30.6% power of predictability. Furthermore, it was found that TC, YoP, and JS have meaningful potential in the linear model. Moreover, TC is negatively correlated with YoP, JS, and positively with TWC. As a result, size of article title, years passed since publication and the journal in which article accepted are good but not sufficient predictor of article citations. In addition, article is a multi-characteristic subject and other predictors can be supposed. However, we think that finding an efficient statistical linear predication model for TC, by increase of articles citation, is overwhelming.