The Role Of Citation Context In Predicting Long-Term Citation Profiles: An Experimental Study Based On A Massive Bibliographic Text Dataset

The impact and significance of a scientific publication is measured mostly by the number of citations it accumulates over the years. Early prediction of the citation profile of research articles is a sig-nificant as well as challenging problem. In this paper, we argue that features gathered from the citation contexts of the research papers can be very relevant for citation prediction. Analyzing a massive dataset of nearly 1.5 million computer science articles and more than 26 million citation contexts, we show that average countX (number of times a paper is cited within the same article) and aver-age citeWords (number of words within the citation context) dis-criminate between various citation ranges as well as citation cat-egories. We use these features in a stratified learning framework for future citation prediction. Experimental results show that the proposed model significantly outperforms the existing citation pre-diction models by a margin of 8-10 % on an average under various experimental settings. Specifically, the features derived from the citation context help in predicting long-term citation behavior