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Reliable and robust hate speech detection (HSD) must therefore be balanced with individuals’ right to privacy. The work analyzes how HSD systems may inadvertently encode authorship-related signals, creating privacy risks. It introduces the privacy-HSD trade-off, benchmarks text privatization methods, and proposes the domain-specific AGNOSPEECH technique to balance privacy and HSD performance.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/introducing-the-privacy-hsd-trade-off-hate-speech-detection-but-not-at-the-cost-of-privacy-abstract/273617/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/introducing-the-privacy-hsd-trade-off-hate-speech-detection-but-not-at-the-cost-of-privacy-abstract/273617.png","ImageObject",300,407,{"name":92,"@type":93},"Theodore","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-08","2026-09-15",true,{"@type":102,"interactionType":103,"userInteractionCount":52},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What problem does the paper address regarding hate speech detection?","Question",{"text":112,"@type":113},"The paper argues that building effective hate speech detection must be balanced with the right to privacy, because systems may encode authorship cues rather than only hate speech signals.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"What is the privacy-HSD trade-off?",{"text":117,"@type":113},"It is the concept that improving hate speech detection can come at the cost of privacy, and it requires careful balancing to limit privacy leakage while maintaining detection utility.",{"name":119,"@type":110,"acceptedAnswer":120},"How do the authors evaluate privacy vs. hate speech detection?",{"text":121,"@type":113},"They run probing studies on datasets, benchmark text privatization methods, and introduce a domain-specific AGNOSPEECH technique, comparing effectiveness in managing the privacy-HSD trade-off.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},273617,1789472290,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":52,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":129,"read_time":144,"preview_page_count":4},7971461740886,"https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2","Introducing the Privacy-HSD Trade-off:  \nHate Speech Detection, but not at the Cost of Privacy Stephen Meisenbacher1,2,3 , Vlad Garbuz4 , Chirill Donos5 , Maxim Dnestreanschii4 , Gabriel Creanga4 , Andreea-Elena Bodea1 , Thomas Lampert6 , Jana Diesner1,2,3  \n1Technical University of Munich, Chair of Human-Centered Computing, Munich, Germany  \n2Munich Center for Machine Learning, Munich, Germany  \n3Munich Data Science Institute, Munich, Germany  \n4University of Southern Denmark, Sønderborg, Denmark  \n5Technical University of Moldova, Chis, inu, Republic of Moldova  \n6ICube, University of Strasbourg, Strasbourg, France  \nCorrespondence: [stephen.meisenbacher@tum.de](stephen.meisenbacher@tum.de)  \narXiv : 2608 . 19006v 1 [cs .CL] 19 Aug 2026  \nAbstract  \nHate speech is a real and timely threat that affects a large portion of online users, especially youth and minority groups. While building reliable and robust automatic hate speech detection (HSD) systems is paramount, we argue that this must also be balanced with the individual right to privacy. Exploring the intersection of HSD and privacy, we demonstrate that HSD systems might unintentionally achieve performance atthe cost of encoding authorship, posing a threat to privacy. Building on these findings, we establish the notion of a privacy-HSD trade-off, which demands a careful balance. We benchmark a series of text privatization methods, as well as our newly proposed domain-specific AGNOSPEECH technique, showing that balancing privacy and HSD is difficult but feasible.  \nThe findings make a strong case for more research on the trade-offs between privacy and HSD, both of which have tangible implications for the safeguarding of online participation.  \n1 Introduction  \nAs the venue of social discourse continues to shift from the public sphere to online environments, the threat of online hate speech has unfortunately also grown rapidly in prevalence (Alkomah and Ma, 2022 ; Gandhi et al., 2024) . A recent 2025 UNESCO report 1 found that nearly 67% of online users have encountered hate speech at some point, and in the European Union, this number is nearly 50% among young adults (aged 16 to 29)2. Alarmingly, the United Nations has found that almost 70% of individuals who personally experience online hate are part of a minority group3. These statistics paint a reality in which hate speech has become  \n1 [http://unesco.org/en/world-media-trends](http://unesco.org/en/world-media-trends)  \n2 [https://ec.europa.eu/eurostat/web/products-eurostat-news/](https://ec.europa.eu/eurostat/web/products-eurostat-news/)[ ](https://ec.europa.eu/eurostat/web/products-eurostat-news/)w/ddn-20240801-1  \n3 [https://www.un.org/en/hate-speech/impact-and-prevention/](https://www.un.org/en/hate-speech/impact-and-prevention/)[ ](https://www.un.org/en/hate-speech/impact-and-prevention/)targets-of-hate  \na serious and pervasive online threat, for which immediate mitigations are required.  \nIn response to the increasing threat of online hate speech, parliamentary bodies such as the Council of Europe have released recommendations for combating hate speech, such as in the recent CM/REC(2022)164, as an integral part of protecting the rights and freedoms of individuals. In the academic literature, both predating and following this recommendation, the response has been immense. Automatic approaches to hate speech detection (HSD), powered by Natural Language Processing and more recently LLMs, have grown in research attention (Fortuna and Nunes, 2018 ; Alkomah and Ma, 2022 ; Gandhi et al., 2024 ; Albladi et al., 2025) . The diversity in which such approaches have addressed hate speech has been the subject of several recent surveys (ibid), and they often center on HSD in the context of social media platforms (Mansur et al., 2023 ; Rawat et al., 2024) . A novel and unexplored angle in the pursuit of automatic HSD comes with its implications on privacy. In other words, automatic and reliable HSD should ideally not depend or","cbCaiirLYXsMjkhb","https://ap.wps.com/l/cbCaiirLYXsMjkhb","pdf",432245,13,"English","# Introduction\n## Privacy-HSD trade-off\n## Datasets and probing study\n## Formalization and case study\n## AGNOSPEECH method and benchmarking","[{\"question\":\"What problem does the paper address regarding hate speech detection?\",\"answer\":\"The paper argues that building effective hate speech detection must be balanced with the right to privacy, because systems may encode authorship cues rather than only hate speech signals.\"},{\"question\":\"What is the privacy-HSD trade-off?\",\"answer\":\"It is the concept that improving hate speech detection can come at the cost of privacy, and it requires careful balancing to limit privacy leakage while maintaining detection utility.\"},{\"question\":\"How do the authors evaluate privacy vs. hate speech detection?\",\"answer\":\"They run probing studies on datasets, benchmark text privatization methods, and introduce a domain-specific AGNOSPEECH technique, comparing effectiveness in managing the privacy-HSD trade-off.\"}]","Introducing the Privacy-HSD Trade-off - Hate Speech Detection - but not at the Cost of Privacy - Abstract | PDF",33]