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        <title>Predicting Long-Term Citations from Short-Term Linguistic Influence</title>
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        <namePart type="given">Sandeep</namePart>
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            <namePart type="given">Zornitsa</namePart>
            <namePart type="family">Kozareva</namePart>
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            <namePart type="given">Yue</namePart>
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    <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 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.</abstract>
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    <identifier type="doi">10.18653/v1/2022.findings-emnlp.418</identifier>
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        <date>2022-12</date>
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%0 Conference Proceedings
%T Predicting Long-Term Citations from Short-Term Linguistic Influence
%A Soni, Sandeep
%A Bamman, David
%A Eisenstein, Jacob
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Findings of the Association for Computational Linguistics: EMNLP 2022
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F soni-etal-2022-predicting
%X 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 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.
%R 10.18653/v1/2022.findings-emnlp.418
%U https://aclanthology.org/2022.findings-emnlp.418
%U https://doi.org/10.18653/v1/2022.findings-emnlp.418
%P 5700-5716

Markdown (Informal)

[Predicting Long-Term Citations from Short-Term Linguistic Influence](https://aclanthology.org/2022.findings-emnlp.418) (Soni et al., Findings 2022)

• Predicting Long-Term Citations from Short-Term Linguistic Influence (Soni et al., Findings 2022)

ACL

• Sandeep Soni, David Bamman, and Jacob Eisenstein. 2022. Predicting Long-Term Citations from Short-Term Linguistic Influence. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 5700–5716, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.