Tackling Social Bias against the Poor: A Dataset and Taxonomy on Aporophobia

2025-04-17

AI Summary

This paper addresses the challenges of identifying and mitigating aporophobia—systemic prejudice against impoverished individuals—in social media, where detection is difficult and benchmark resources are lacking. We introduce the first high-quality, fine-grained English aporophobia dataset for Twitter, covering multiple geographic regions; operationalize aporophobia in social media contexts for the first time; and propose a discourse-level classification framework distinguishing discriminatory utterances from anti-discriminatory responses. Methodologically, we integrate cross-regional corpus collection, multi-annotator human labeling, Transformer-based multiclass modeling, and in-depth error analysis. Key contributions include: (1) releasing the first publicly available, expert-validated aporophobia benchmark dataset; (2) establishing an extensible, interpretable fine-grained classification framework; and (3) empirically identifying core detection challenges—including irony and implicit expression—that hinder automated bias monitoring, thereby providing theoretical foundations and empirical evidence for bias surveillance, policy evaluation, and intervention tool development.

Technology Category

• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy
• Computer Vision: Bias, Fairness & Privacy
• Application Domains: Humanities & Computational Social Science

Application Category

• Social Networks and Social Media: Fairness and bias in social network and social media analysis
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Responsible Web: Social and technical mechanisms of refusal for web technologies and applications

Abstract

Eradicating poverty is the first goal in the United Nations Sustainable Development Goals. However, aporophobia -- the societal bias against people living in poverty -- constitutes a major obstacle to designing, approving and implementing poverty-mitigation policies. This work presents an initial step towards operationalizing the concept of aporophobia to identify and track harmful beliefs and discriminative actions against poor people on social media. In close collaboration with non-profits and governmental organizations, we conduct data collection and exploration. Then we manually annotate a corpus of English tweets from five world regions for the presence of (1) direct expressions of aporophobia, and (2) statements referring to or criticizing aporophobic views or actions of others, to comprehensively characterize the social media discourse related to bias and discrimination against the poor. Based on the annotated data, we devise a taxonomy of categories of aporophobic attitudes and actions expressed through speech on social media. Finally, we train several classifiers and identify the main challenges for automatic detection of aporophobia in social networks. This work paves the way towards identifying, tracking, and mitigating aporophobic views on social media at scale.

Problem

• Identifying societal bias against the poor on social media
• Developing a taxonomy for aporophobic attitudes in online speech
• Automating detection of aporophobia to mitigate discriminatory views

Innovation

• Manual annotation of English tweets for aporophobia
• Taxonomy of aporophobic attitudes from social media
• Training classifiers for automatic aporophobia detection