arXiv 2507.043502025-07-06cs.CL

HatePRISM：政策、平台与研究整合——推进自然语言处理在仇恨言论主动缓解中的应用

HatePRISM: Policies, Platforms, and Research Integration. Advancing NLP for Hate Speech Proactive Mitigation

IIT Kharagpur（印度理工学院Kharagpur分校）；University of Hamburg（汉堡大学）；Technical University of Munich（慕尼黑技术大学）；Bucerius Law School（布勒斯海姆法学院）；Skolkovo Institute of Science and Technology（斯克尔科维科学与技术研究所）；Artificial Intelligence Research Institute（人工智能研究 institute）

Naquee Rizwan, Seid Muhie Yimam, Daryna Dementieva, Florian Skupin, Tim Fischer, Daniil Moskovskiy, Aarushi Ajay Borkar, Robert Geislinger, Punyajoy Saha, Sarth… 展开作者

Naquee Rizwan, Seid Muhie Yimam, Daryna Dementieva, Florian Skupin, Tim Fischer, Daniil Moskovskiy, Aarushi Ajay Borkar, Robert Geislinger, Punyajoy Saha, Sarthak Roy, Martin Semmann, Alexander Panchenko, Chris Biemann, Animesh Mukherjee

备注：: arXiv admin note: substantial text overlap with arXiv:2406.19543

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AI总结：

HatePRISM从国家法规、平台政策和NLP数据集三方面系统审视仇恨言论治理，揭示定义与实践的不一致，并提出融合多种策略的统一自动化审核框架研究方向。

摘要：

尽管各国和社交媒体平台施加了各种规定，例如（印度政府，2021；欧洲议会与欧盟理事会，2022）等，仇恨内容仍然是一个重大挑战。现有方法主要依赖反应性措施，如屏蔽或暂停攻击性消息，而新兴策略则侧重于主动措施，如去毒化处理和反言论。在我们称为HatePRISM的工作中，我们从三个视角对仇恨言论法规和策略进行了全面审视：国家法规、社交平台政策以及自然语言处理研究数据集。我们的发现揭示了各司法管辖区和平台之间在仇恨言论定义和审核实践上存在显著不一致，同时与研究工作的对齐也存在不足。基于这些见解，我们提出了进一步探索一个融合多种策略的自动化仇恨言论审核统一框架的思路和研究方向。

英文摘要：

Despite regulations imposed by nations and social media platforms, e.g. (Government of India, 2021; European Parliament and Council of the European Union, 2022), inter alia, hateful content persists as a significant challenge. Existing approaches primarily rely on reactive measures such as blocking or suspending offensive messages, with emerging strategies focusing on proactive measurements like detoxification and counterspeech. In our work, which we call HatePRISM, we conduct a comprehensive examination of hate speech regulations and strategies from three perspectives: country regulations, social platform policies, and NLP research datasets. Our findings reveal significant inconsistencies in hate speech definitions and moderation practices across jurisdictions and platforms, alongside a lack of alignment with research efforts. Based on these insights, we suggest ideas and research direction for further exploration of a unified framework for automated hate speech moderation incorporating diverse strategies.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。