Good Intentions Beyond ACL: Who Does NLP for Social Good, and Where?
Qingcheng Zeng
Abstract
The study reveals that ACL authors are more likely to address social good concerns in non-ACL venues, and most NLP4SG publications are from non-ACL authors.
The social impact of Natural Language Processing (NLP) is increasingly important, with a rising community focus on initiatives related to NLP for Social Good (NLP4SG). Indeed, in recent years, almost 20% of all papers in the ACL Anthology address topics related to social good as defined by the UN Sustainable Development Goals (Adauto et al., 2023). In this study, we take an author- and venue-level perspective to map the landscape of NLP4SG, quantifying the proportion of work addressing social good concerns both within and beyond the ACL community, by both core ACL contributors and non-ACL authors. With this approach we discover two surprising facts about the landscape of NLP4SG. First, ACL authors are dramatically more likely to do work addressing social good concerns when publishing in venues outside of ACL. Second, the vast majority of publications using NLP techniques to address concerns of social good are done by non-ACL authors in venues outside of ACL. We discuss the implications of these findings on agenda-setting considerations for the ACL community related to NLP4SG.
Community
Good Intentions Beyond ACL: Who Does NLP for Social Good, and Where?
This is an automated message from the Librarian Bot. I found the following papers similar to this paper.
The following papers were recommended by the Semantic Scholar API
- The Rise of AfricaNLP: Contributions, Contributors, and Community Impact (2005-2025) (2025)
- Exploring Subjective Tasks in Farsi: A Survey Analysis and Evaluation of Language Models (2025)
- Charting a Decade of Computational Linguistics in Italy: The CLiC-it Corpus (2025)
- How much are LLMs changing the language of academic papers after ChatGPT? A multi-database and full text analysis (2025)
- A Multi-Level Benchmark for Causal Language Understanding in Social Media Discourse (2025)
- Detecting Corpus-Level Knowledge Inconsistencies in Wikipedia with Large Language Models (2025)
- COLE: a Comprehensive Benchmark for French Language Understanding Evaluation (2025)
Please give a thumbs up to this comment if you found it helpful!
If you want recommendations for any Paper on Hugging Face checkout this Space
You can directly ask Librarian Bot for paper recommendations by tagging it in a comment:
@librarian-bot
recommend
Models citing this paper 0
No model linking this paper
Datasets citing this paper 0
No dataset linking this paper
Spaces citing this paper 0
No Space linking this paper
Collections including this paper 0
No Collection including this paper