# Geographic Citation Gaps in NLP Research

arXiv: https://arxiv.org/abs/2210.14424 · HTML: https://mlsanity.com/paper/2210.14424/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 53 references that are in the map.
- First posted on arXiv 26 Oct 2022.

## Builds on (most cited first, 4 of 4)

- [Gender Gap in Natural Language Processing Research: Disparities in Authorship and Citations](https://mlsanity.com/paper/2005.00962/) (2020)
- [Historical comparison of gender inequality in scientific careers across countries and disciplines](https://mlsanity.com/paper/1907.04103/) (2019)
- [The State of NLP Literature: A Diachronic Analysis of the ACL Anthology](https://mlsanity.com/paper/1911.03562/) (2019)
- [How Does Author Affiliation Affect Preprint Citation Count? Analyzing Citation Bias at the Institution and Country Level](https://mlsanity.com/paper/2205.02033/) (2022)

## Built on by (most cited first, 19 of 19)

- [It's MBR All the Way Down: Modern Generation Techniques Through the Lens of Minimum Bayes Risk](https://mlsanity.com/paper/2310.01387/) (2023)
- [A Material Lens on Coloniality in NLP](https://mlsanity.com/paper/2311.08391/) (2023)
- [We are Who We Cite: Bridges of Influence Between Natural Language Processing and Other Academic Fields](https://mlsanity.com/paper/2310.14870/) (2023)
- [Forgotten Knowledge: Examining the Citational Amnesia in NLP](https://mlsanity.com/paper/2305.18554/) (2023)
- [Exploring the Landscape of Natural Language Processing Research](https://mlsanity.com/paper/2307.10652/) (2023)
- [Pre-Trained Language Models Represent Some Geographic Populations Better Than Others](https://mlsanity.com/paper/2403.11025/) (2024)
- [Citation Amnesia: On The Recency Bias of NLP and Other Academic Fields](https://mlsanity.com/paper/2402.12046/) (2024)
- [Standard Language Ideology in AI-Generated Language](https://mlsanity.com/paper/2406.08726/) (2024)
- [To Build Our Future, We Must Know Our Past: Contextualizing Paradigm Shifts in Natural Language Processing](https://mlsanity.com/paper/2310.07715/) (2023)
- [Shoulders of Giants: A Look at the Degree and Utility of Openness in NLP Research](https://mlsanity.com/paper/2406.06021/) (2024)
- [On Tables with Numbers, with Numbers](https://mlsanity.com/paper/2408.06062/) (2024)
- [Transforming Scholarly Landscapes: Influence of Large Language Models on Academic Fields beyond Computer Science](https://mlsanity.com/paper/2409.19508/) (2024)
- [Large Multimodal Models for Low-Resource Languages: A Survey](https://mlsanity.com/paper/2502.05568/) (2025)
- [One Venue, Two Conferences: The Separation of Chinese and American Citation Networks](https://mlsanity.com/paper/2211.12424/) (2022)
- [Is there really a Citation Age Bias in NLP?](https://mlsanity.com/paper/2401.03545/) (2024)
- [A Note on Bias to Complete](https://mlsanity.com/paper/2402.11710/) (2024)
- [Modeling the Sacred: Considerations when Using Religious Texts in Natural Language Processing](https://mlsanity.com/paper/2404.14740/) (2024)
- [Big Tech-Funded AI Papers Have Higher Citation Impact, Greater Insularity, and Larger Recency Bias](https://mlsanity.com/paper/2512.05714/) (2025)
- [The Future of NLP may not be at NLP Conferences: Scholarly Migration Patterns in Natural Language Processing](https://mlsanity.com/paper/2607.02416/) (2026)

## Abstract (arXiv)

In a fair world, people have equitable opportunities to education, to conduct scientific research, to publish, and to get credit for their work, regardless of where they live. However, it is common knowledge among researchers that a vast number of papers accepted at top NLP venues come from a handful of western countries and (lately) China; whereas, very few papers from Africa and South America get published. Similar disparities are also believed to exist for paper citation counts. In the spirit of "what we do not measure, we cannot improve", this work asks a series of questions on the relationship between geographical location and publication success (acceptance in top NLP venues and citation impact). We first created a dataset of 70,000 papers from the ACL Anthology, extracted their meta-information, and generated their citation network. We then show that not only are there substantial geographical disparities in paper acceptance and citation but also that these disparities persist even when controlling for a number of variables such as venue of publication and sub-field of NLP. Further, despite some steps taken by the NLP community to improve geographical diversity, we show that the disparity in publication metrics across locations is still on an increasing trend since the early 2000s. We release our code and dataset here: https://github.com/iamjanvijay/acl-cite-net