Impact Factor of a Term: a Tool for Assessing Article's Future Citations and Author's Influence Based on PubMed and DBLP Collections

This article describes a new bibliometric indicator called Impact Factor of a Term (IFT) that helps to predict future impact of scientific works and/or the author. The predictive properties of IFT are proven by two examples of different collections of scientific articles. It is shown that the correlations of the current and future IFT values depending on the trend are practically similar for both collections. The graphs of IFT correlations of the current and future years depending on the number of articles with the word/term are presented. The graphs show that the higher the current frequency of the term and the number of articles with this term, the greater the correlation and stability of IFT. The stability of IFT helps to accurately predict the number of future citations. The list of the most informative words/terms with the largest total values of IFT multiplied by the current frequency is analyzed. It has been shown that the size of collection affects the stability and predictive properties of IFT. The words and terms with high IFT values allow us to judge the future impact of an article and its author based on the prediction of future citations. Such words also help identify promising research directions.