Why do we Hate Migrants? A Double Machine Learning-based Approach
Aparup Khatua, L3S Research Center, Leibniz University Hannover, Hannover, Germany (khatua@l3s.de) · Wolfgang Nejdl, L3S Research Center, Leibniz University Hannover, Hannover, Germany (nejdl@l3s.de)
Published in HT '23: 34th ACM Conference on Hypertext and Social Media · DOI: 10.1145/3603163.3609040 · License: © Copyright held by the owner/author(s). Publication rights licensed to ACM.
Authors: Aparup Khatua, Wolfgang Nejdl
Keywords: Causality, Double machine Learning, Online Hate, Toxicity
Session: Social and Intelligent Media: Through the mirror of social media
Pages: spit
Conference: HT '23
Abstract
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.
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