Abstract
In bibliometric and scientometric research, the quantitative assessment of scientific impact has boomed over the past few decades. Citations, being playing a major role in enhancing the impact of researchers, have become a very significant part of a plethora of new techniques for measuring scientific impact. Self-citations, though can be used genuinely to credit someone's own work, can play a significant role in artificial manipulation of scientific impact. In this research, we study the impact of self-citations on enhancing the scientific impact of an author using a dataset retrieved from AMiner ranging from 1936 to 2014 from the computer science domain. We investigated the relations among trends of self-citation and their influence on scientific impact. We also studied its influence on ranking metrics including author impact factor and H-Index. By analyzing self-citations over time, we discover five basic self-citation trends, which are early, middle, later, multi and none. Distinctly different patterns were observed in self-citations trends. The results show that self-citations, if totally removed from total received citations, negatively influence the AIF and H-Index values and hence can be used to artificially boost the scientific impact. We used regression-based prediction models to predict the influence of self-citations on future H-Index. Classifiers including Logistic Regression, Naive Bayes and K-NN were used with an accuracy of 93%, 73% and 60% respectively.