Predicting Long-Term Citations from Short-Term Linguistic Influence
- URL: http://arxiv.org/abs/2210.13628v1
- Date: Mon, 24 Oct 2022 22:03:26 GMT
- Title: Predicting Long-Term Citations from Short-Term Linguistic Influence
- Authors: Sandeep Soni and David Bamman and Jacob Eisenstein
- Abstract summary: A standard measure of the influence of a research paper is the number of times it is cited.
We propose a novel method to quantify linguistic influence in timestamped document collections.
- Score: 20.78217545537925
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: A standard measure of the influence of a research paper is the number of
times it is cited. However, papers may be cited for many reasons, and citation
count offers limited information about the extent to which a paper affected the
content of subsequent publications. We therefore propose a novel method to
quantify linguistic influence in timestamped document collections. There are
two main steps: first, identify lexical and semantic changes using contextual
embeddings and word frequencies; second, aggregate information about these
changes into per-document influence scores by estimating a high-dimensional
Hawkes process with a low-rank parameter matrix. We show that this measure of
linguistic influence is predictive of $\textit{future}$ citations: the estimate
of linguistic influence from the two years after a paper's publication is
correlated with and predictive of its citation count in the following three
years. This is demonstrated using an online evaluation with incremental
temporal training/test splits, in comparison with a strong baseline that
includes predictors for initial citation counts, topics, and lexical features.
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