80% of hateful tweets remain online after five months with no higher removal rate than non-hateful content, while human-AI moderation pipelines can feasibly cut user exposure below regulatory penalty costs.
A 61-million-person experiment in social influence and political mobilization.Nature, 489(7415):295–298
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The authors develop a two-stage orthogonal learning framework using graph neural networks to estimate heterogeneous direct and spillover causal effects on networks, along with bootstrap-based uncertainty quantification.
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The Enforcement and Feasibility of Hate Speech Moderation on Twitter
80% of hateful tweets remain online after five months with no higher removal rate than non-hateful content, while human-AI moderation pipelines can feasibly cut user exposure below regulatory penalty costs.
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Estimating Heterogeneous Causal Effect on Networks via Orthogonal Learning
The authors develop a two-stage orthogonal learning framework using graph neural networks to estimate heterogeneous direct and spillover causal effects on networks, along with bootstrap-based uncertainty quantification.