Details
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 34th ACM Conference on Hypertext and Social Media |
| Number of pages | 10 |
| ISBN (electronic) | 9798400702327 |
| Publication status | Published - 5 Sept 2023 |
Abstract
Keywords
- Causality, Double machine Learning, Online Hate, Toxicity
ASJC Scopus subject areas
- Computer Science(all)
- Software
- Computer Science(all)
- Artificial Intelligence
- Computer Science(all)
- Human-Computer Interaction
- Computer Science(all)
- Computer Graphics and Computer-Aided Design
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Proceedings of the 34th ACM Conference on Hypertext and Social Media. 2023. 35.
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Why do we Hate Migrants?
T2 - A Double Machine Learning-based Approach
AU - Khatua, Aparup
AU - Nejdl, Wolfgang
N1 - Funding Information: Funding for this paper was, in part, provided by the Federal Ministry of Education and Research (BMBF), Germany under the project LeibnizKILabor with grant No. 01DD20003.
PY - 2023/9/5
Y1 - 2023/9/5
N2 - AI-based NLP literature has explored antipathy toward the marginalized section of society, such as migrants, and their social acceptance. Broadly, extant literature has conceptualized this as an online hate speech detection task and employed predictive ML models. However, a crucial omission in this literature is the genesis (or causality) of online hate, i.e., why do we hate migrants? Drawing insights from social science literature, we have identified three antecedents of online hate: Cultural, Economic, and Security concerns. Subsequently, we probe -which of these concerns triggers higher toxicity on online platforms? Initially, we consider OLS-based regression analysis and SHAP framework to identify the predictors of toxicity, and subsequently, we use Double Machine Learning (DML)-based casual analysis to investigate whether good predictors of toxicity are also causally significant. We find that the causal effect of Cultural concerns on toxicity is higher than Security and Economic concerns.
AB - AI-based NLP literature has explored antipathy toward the marginalized section of society, such as migrants, and their social acceptance. Broadly, extant literature has conceptualized this as an online hate speech detection task and employed predictive ML models. However, a crucial omission in this literature is the genesis (or causality) of online hate, i.e., why do we hate migrants? Drawing insights from social science literature, we have identified three antecedents of online hate: Cultural, Economic, and Security concerns. Subsequently, we probe -which of these concerns triggers higher toxicity on online platforms? Initially, we consider OLS-based regression analysis and SHAP framework to identify the predictors of toxicity, and subsequently, we use Double Machine Learning (DML)-based casual analysis to investigate whether good predictors of toxicity are also causally significant. We find that the causal effect of Cultural concerns on toxicity is higher than Security and Economic concerns.
KW - Causality
KW - Double machine Learning
KW - Online Hate
KW - Toxicity
UR - http://www.scopus.com/inward/record.url?scp=85174318700&partnerID=8YFLogxK
U2 - 10.1145/3603163.3609040
DO - 10.1145/3603163.3609040
M3 - Conference contribution
SN - 979-8-4007-0232-7
BT - Proceedings of the 34th ACM Conference on Hypertext and Social Media
ER -