Online hate speech poses a serious threat to individual well-being and societal cohesion. A promising solution to curb online hate speech is counterspeech--that is, responding to hateful posts with messages that discourage such behavior. Persuasion and communication theory suggest that contextualized counterspeech, which directly references the content of a hateful message, is more persuasive than generic messages but may also lead to psychological reactance; however, systematic evidence on how offenders react to contextualized counterspeech is missing. In this paper, we analyze whether contextualized counterspeech is more effective than non-contextualized counterspeech in reducing online hate speech. To do so, we used generative AI, specifically large language models (LLMs), as a methodological tool to generate contextualized counterspeech in a consistent and reproducible manner. We then conducted a large-scale, pre-registered field experiment (N=2,664) on the social media platform Twitter/X. Users who had posted hateful content were randomly assigned to receive either contextualized, non-contextualized, or no counterspeech. We further varied the counterspeech strategy by comparing messages that promoted empathy vs. messages that warned about the consequences of online misbehavior, strategies that had been proven to be effective for non-contextualized counterspeech. We find that non-contextualized counterspeech employing a warning-of-consequences strategy significantly reduces online hate speech. By contrast, contextualized counterspeech generated by LLMs does not improve effectiveness and may even backfire. These findings suggest that contextualization can backfire in the specific context of hateful online interactions, with important implications for the design of counterspeech interventions in practice, for example, by users or civil society organizations.

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