# Internal and External Impacts of Natural Language Processing Papers

arXiv: https://arxiv.org/abs/2505.16061 · HTML: https://mlsanity.com/paper/2505.16061/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 14 references that are in the map.
- First posted on arXiv 21 May 2025.

## Builds on (most cited first, 5 of 5)

- [SPECTER: Document-level Representation Learning using Citation-informed Transformers](https://mlsanity.com/paper/2004.07180/) (2020)
- [OpenAlex: A fully-open index of scholarly works, authors, venues, institutions, and concepts](https://mlsanity.com/paper/2205.01833/) (2022)
- [Structural Scaffolds for Citation Intent Classification in Scientific Publications](https://mlsanity.com/paper/1904.01608/) (2019)
- [Overton -- A bibliometric database of policy document citations](https://mlsanity.com/paper/2201.07643/) (2022)
- [Breaking Out of the Ivory Tower: A Large-scale Analysis of Patent Citations to HCI Research](https://mlsanity.com/paper/2301.13431/) (2023)

## Abstract (arXiv)

We investigate the impacts of NLP research published in top-tier conferences (i.e., ACL, EMNLP, and NAACL) from 1979 to 2024. By analyzing citations from research articles and external sources such as patents, media, and policy documents, we examine how different NLP topics are consumed both within the academic community and by the broader public. Our findings reveal that language modeling has the widest internal and external influence, while linguistic foundations have lower impacts. We also observe that internal and external impacts generally align, but topics like ethics, bias, and fairness show significant attention in policy documents with much fewer academic citations. Additionally, external domains exhibit distinct preferences, with patents focusing on practical NLP applications and media and policy documents engaging more with the societal implications of NLP models.