Tackling Social Bias against the Poor: A Dataset and Taxonomy on Aporophobia
This work addresses the obstacle of aporophobia in poverty-mitigation policies by enabling large-scale identification and tracking of harmful beliefs on social media, though it is incremental as it builds on existing concepts and methods.
The authors tackled the problem of societal bias against the poor (aporophobia) by creating a manually annotated dataset of English tweets from five world regions and devising a taxonomy of aporophobic attitudes and actions, training classifiers for automatic detection and identifying key challenges.
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.