Good Intentions Beyond ACL: Who Does NLP for Social Good, and Where?

2025-10-05

AI Summary

This study investigates the distribution of NLP4SG (Natural Language Processing for Social Good) research within the academic community, specifically examining disparities in engagement with United Nations Sustainable Development Goals (SDGs) between core ACL authors and non-ACL authors. Method: Using bibliometric analysis, we systematically examine author affiliations, publication venues, and thematic coverage of NLP4SG papers in the ACL Anthology and major non-ACL conferences/journals. Contribution/Results: Over 80% of NLP4SG publications originate from non-ACL authors publishing outside the ACL ecosystem; core ACL contributors publish disproportionately fewer NLP4SG papers, and when they do, they predominantly choose non-ACL venues. These findings reveal a structural underrepresentation of the ACL community in setting the NLP4SG research agenda—challenging the prevailing assumption of ACL dominance in this domain—and provide empirical support for diversifying the scholarly ecosystem toward responsible, socially impactful NLP.

Technology Category

• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)
• Application Domains: Humanities & Computational Social Science
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community

Application Category

• Web Mining and Content Analysis: Large pretrained models with web data
• Social Networks and Social Media: Fairness and bias in social network and social media analysis
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

Abstract

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.

Problem

• Mapping NLP for Social Good research distribution
• Analyzing ACL vs non-ACL author contributions
• Quantifying social good publications across venues

Innovation

• Mapping NLP4SG landscape via author analysis
• Quantifying social good work inside and outside ACL
• Comparing ACL and non-ACL author contributions