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In the context of escalating hate speech and polarization on social media,this study investigates the potential of employing responses generated by LargeLanguage Models (LLM), complemented with pertinent verified knowledge links, tocounteract such trends. Through extensive A/B testing involving the posting of753 automatically generated responses, the goal was to minimize the propagationof hate speech directed at Ukrainian refugees in Poland.  The results indicate that deploying LLM-generated responses as replies toharmful tweets effectively diminishes user engagement, as measured bylikes/impressions. When we respond to an original tweet, i.e., which is not areply, we reduce the engagement of users by over 20\% without increasing thenumber of impressions. On the other hand, our responses increase the ratio ofthe number of replies to a harmful tweet to impressions, especially if theharmful tweet is not original. Additionally, the study examines how generatedresponses influence the overall sentiment of tweets in the discussion,revealing that our intervention does not significantly alter the meansentiment.  This paper suggests the implementation of an automatic moderation system tocombat hate speech on social media and provides an in-depth analysis of the A/Bexperiment, covering methodology, data collection, and statistical outcomes.Ethical considerations and challenges are also discussed, offering guidance forthe development of discourse moderation systems leveraging the capabilities ofgenerative AI.

Analyzing the Influence of Language Model-Generated Responses in Mitigating Hate Speech on Social Media Directed at Ukrainian Refugees in Poland