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

arXiv: https://arxiv.org/abs/2504.13085 · HTML: https://mlsanity.com/paper/2504.13085/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 23 of its 112 references that are in the map.
- First posted on arXiv 17 Apr 2025.

## Builds on (most cited first, 23 of 23)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [Language (Technology) is Power: A Critical Survey of "Bias" in NLP](https://mlsanity.com/paper/2005.14050/) (2020)
- [BERTopic: Neural topic modeling with a class-based TF-IDF procedure](https://mlsanity.com/paper/2203.05794/) (2022)
- [TweetEval: Unified Benchmark and Comparative Evaluation for Tweet Classification](https://mlsanity.com/paper/2010.12421/) (2020)
- [BERTweet: A pre-trained language model for English Tweets](https://mlsanity.com/paper/2005.10200/) (2020)
- [Directions in Abusive Language Training Data: Garbage In, Garbage Out](https://mlsanity.com/paper/2004.01670/) (2020)
- [The Geometry of Culture: Analyzing Meaning through Word Embeddings](https://mlsanity.com/paper/1803.09288/) (2018)
- [Handling Bias in Toxic Speech Detection: A Survey](https://mlsanity.com/paper/2202.00126/) (2022)
- [SemEval-2023 Task 10: Explainable Detection of Online Sexism](https://mlsanity.com/paper/2303.04222/) (2023)
- [Exploring Cross-Cultural Differences in English Hate Speech Annotations: From Dataset Construction to Analysis](https://mlsanity.com/paper/2308.16705/) (2023)
- [Confronting Abusive Language Online: A Survey from the Ethical and Human Rights Perspective](https://mlsanity.com/paper/2012.12305/) (2020)
- [On Cross-Dataset Generalization in Automatic Detection of Online Abuse](https://mlsanity.com/paper/2010.07414/) (2020)
- [Detecting weak and strong Islamophobic hate speech on social media](https://mlsanity.com/paper/1812.10400/) (2018)
- [The Limits of Global Inclusion in AI Development](https://mlsanity.com/paper/2102.01265/) (2021)
- [DisCGen: A Framework for Discourse-Informed Counterspeech Generation](https://mlsanity.com/paper/2311.18147/) (2023)
- [Subjective textitIsms? On the Danger of Conflating Hate and Offence in Abusive Language Detection](https://mlsanity.com/paper/2403.02268/) (2024)
- [OATH-Frames: Characterizing Online Attitudes Towards Homelessness with LLM Assistants](https://mlsanity.com/paper/2406.14883/) (2024)
- [The crime of being poor](https://mlsanity.com/paper/2303.14128/) (2023)
- [Can Poverty Be Reduced by Acting on Discrimination? An Agent-based Model for Policy Making](https://mlsanity.com/paper/2403.01600/) (2024)
- [Challenging Negative Gender Stereotypes: A Study on the Effectiveness of Automated Counter-Stereotypes](https://mlsanity.com/paper/2404.11845/) (2024)

## Abstract (arXiv)

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.