@inproceedings{soni-etal-2022-predicting,
    title = "Predicting Long-Term Citations from Short-Term Linguistic Influence",
    author = "Soni, Sandeep  and
      Bamman, David  and
      Eisenstein, Jacob",
    editor = "Goldberg, Yoav  and
      Kozareva, Zornitsa  and
      Zhang, Yue",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2022",
    month = dec,
    year = "2022",
    address = "Abu Dhabi, United Arab Emirates",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2022.findings-emnlp.418/",
    doi = "10.18653/v1/2022.findings-emnlp.418",
    pages = "5700--5716",
    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 is not informative 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 parameters by estimating a high-dimensional Hawkes process with a low-rank parameter matrix. The resulting measures of linguistic influence are predictive of \textit{future} citations. Specifically, 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."
}