@inproceedings{brate-etal-2024-bayesian,
title = "A {B}ayesian Quantification of Aporophobia and the Aggravating Effect of Low{--}Wealth Contexts on Stigmatization",
author = "Brate, Ryan and
Van Erp, Marieke and
Van Den Bosch, Antal",
editor = {Chung, Yi-Ling and
Talat, Zeerak and
Nozza, Debora and
Plaza-del-Arco, Flor Miriam and
R{\"o}ttger, Paul and
Mostafazadeh Davani, Aida and
Calabrese, Agostina},
booktitle = "Proceedings of the 8th Workshop on Online Abuse and Harms (WOAH 2024)",
month = jun,
year = "2024",
address = "Mexico City, Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.woah-1.18/",
doi = "10.18653/v1/2024.woah-1.18",
pages = "234--243",
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."
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%0 Conference Proceedings
%T A Bayesian Quantification of Aporophobia and the Aggravating Effect of Low–Wealth Contexts on Stigmatization
%A Brate, Ryan
%A Van Erp, Marieke
%A Van Den Bosch, Antal
%Y Chung, Yi-Ling
%Y Talat, Zeerak
%Y Nozza, Debora
%Y Plaza-del-Arco, Flor Miriam
%Y Röttger, Paul
%Y Mostafazadeh Davani, Aida
%Y Calabrese, Agostina
%S Proceedings of the 8th Workshop on Online Abuse and Harms (WOAH 2024)
%D 2024
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico
%F brate-etal-2024-bayesian
%X 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.
%R 10.18653/v1/2024.woah-1.18
%U https://aclanthology.org/2024.woah-1.18/
%U https://doi.org/10.18653/v1/2024.woah-1.18
%P 234-243
Markdown (Informal)
[A Bayesian Quantification of Aporophobia and the Aggravating Effect of Low–Wealth Contexts on Stigmatization](https://aclanthology.org/2024.woah-1.18/) (Brate et al., WOAH 2024)
ACL