The importance of a research article is routinely measured by counting how many times it has been cited. However, treating all citations with equal weight ignores the wide variety of functions that citations perform. The research described in this presentation – work that was performed with Xiaodan Zhu, Peter Turney (National Research Council Canada) and Daniel Lemire (TELUQ, Université du Québec à Montréal) – aims to automatically identify the subset of references in a bibliography that have a central academic influence on the citing paper. To achieve this we examined the effectiveness of a variety of features in the citing paper that might plausibly predict the academic influence of a citation. We asked a group of authors to identify the key references in their own work and created a dataset in which citations were labeled according to their academic influence. Using automatic feature selection with supervised machine learning, we developed a model for predicting academic influence that achieves good performance on this dataset using only four features. The performance of these features inspired us to design an influence-primed h-index (the hip-index). According to our experiments, the hip-index is a better indicator of researcher performance than the conventional h-index.