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Platform-Driven Hate Speech: An Epidemiological Model with Optimal Taxation

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  • Nazaria Solferino

Abstract

Online hate speech is a global challenge amplified by engagement8-driven social media algorithms. This paper develops an epidemiological model of hate speech propagation capturing the strategic interaction between a profit-maximizing platform and a welfare-maximizing government. The platform's profit depends on the prevalence of hate speech and on its own algorithmic reactivity, creating a feedback loop between the epidemic and economic incentives. The government sets an optimal tax on amplification to internalize the social costs, balancing the benefit of tax revenue against the deadweight loss of taxation. The Stackelberg equilibrium is characterised analytically and solved numerically. The optimal tax reduces hate speech prevalence, eliminates bistability and lowers victim harm.

Suggested Citation

  • Nazaria Solferino, 2026. "Platform-Driven Hate Speech: An Epidemiological Model with Optimal Taxation," Papers 2606.08294, arXiv.org.
    • Handle: RePEc:arx:papers:2606.08294
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    File URL: https://arxiv.org/pdf/2606.08294
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    1. Matthew R DeVerna & Rachith Aiyappa & Diogo Pacheco & John Bryden & Filippo Menczer, 2024. "Identifying and characterizing superspreaders of low-credibility content on Twitter," PLOS ONE, Public Library of Science, vol. 19(5), pages 1-17, May.
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