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A standard measure of the influence of a research paper is the number oftimes it is cited. However, papers may be cited for many reasons, and citationcount offers limited information about the extent to which a paper affected thecontent of subsequent publications. We therefore propose a novel method toquantify linguistic influence in timestamped document collections. There aretwo main steps: first, identify lexical and semantic changes using contextualembeddings and word frequencies; second, aggregate information about thesechanges into per-document influence scores by estimating a high-dimensionalHawkes process with a low-rank parameter matrix. We show that this measure oflinguistic influence is predictive of $\textit{future}$ citations: the estimateof linguistic influence from the two years after a paper's publication iscorrelated with and predictive of its citation count in the following threeyears. This is demonstrated using an online evaluation with incrementaltemporal training/test splits, in comparison with a strong baseline thatincludes predictors for initial citation counts, topics, and lexical features.

Predicting Long-Term Citations from Short-Term Linguistic Influence