A Bayesian Quantification of Aporophobia and the Aggravating Effect of Low–Wealth Contexts on Stigmatization

Ryan Brate, Marieke Van Erp, Antal Van Den Bosch


Abstract
Aporophobia, a negative social bias against poverty and the poor, has been highlighted asan overlooked phenomenon in toxicity detec-tion in texts. Aporophobia is potentially im-portant both as a standalone form of toxicity,but also given its potential as an aggravatingfactor in the wider stigmatization of groups. Asyet, there has been limited quantification of thisphenomenon. In this paper, we first quantifythe extent of aporophobia, as observable in Red-dit data: contrasting estimates of stigmatisingtopic propensity between low–wealth contextsand high–wealth contexts via Bayesian estima-tion. Next, we consider aporophobia as a causalfactor in the prejudicial association of groupswith stigmatising topics, by introducing peoplegroup as a variable, specifically Black people.This group is selected given its history of be-ing the subject of toxicity. We evaluate theaggravating effect on the observed n–grams in-dicative of stigmatised topics observed in com-ments which refer to Black people, due to thepresence of low–wealth contexts. We performthis evaluation via a Structural Causal Mod-elling approach, performing interventions onsimulations via Bayesian models, for three hy-pothesised causal mechanisms.
Anthology ID:
2024.woah-1.18
Volume:
Proceedings of the 8th Workshop on Online Abuse and Harms (WOAH 2024)
Month:
June
Year:
2024
Address:
Mexico City, Mexico
Editors:
Yi-Ling Chung, Zeerak Talat, Debora Nozza, Flor Miriam Plaza-del-Arco, Paul Röttger, Aida Mostafazadeh Davani, Agostina Calabrese
Venues:
WOAH | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
234–243
Language:
URL:
https://aclanthology.org/2024.woah-1.18
DOI:
Bibkey:
Cite (ACL):
Ryan Brate, Marieke Van Erp, and Antal Van Den Bosch. 2024. A Bayesian Quantification of Aporophobia and the Aggravating Effect of Low–Wealth Contexts on Stigmatization. In Proceedings of the 8th Workshop on Online Abuse and Harms (WOAH 2024), pages 234–243, Mexico City, Mexico. Association for Computational Linguistics.
Cite (Informal):
A Bayesian Quantification of Aporophobia and the Aggravating Effect of Low–Wealth Contexts on Stigmatization (Brate et al., WOAH-WS 2024)
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PDF:
https://preview.aclanthology.org/jeptaln-2024-ingestion/2024.woah-1.18.pdf