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.- Anthology ID:
- 2022.findings-emnlp.418
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2022
- Month:
- December
- Year:
- 2022
- Address:
- Abu Dhabi, United Arab Emirates
- Editors:
- Yoav Goldberg, Zornitsa Kozareva, Yue Zhang
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 5700–5716
- Language:
- URL:
- https://aclanthology.org/2022.findings-emnlp.418
- DOI:
- 10.18653/v1/2022.findings-emnlp.418
- Cite (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.
- Cite (Informal):
- Predicting Long-Term Citations from Short-Term Linguistic Influence (Soni et al., Findings 2022)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2022.findings-emnlp.418.pdf