让民间组织参与开源社交平台监控工具迭代，提升反民主内容识别能力
Civil Society in the Loop: Feedback-Driven Adaptation of (L)LM-Assisted Classification in an Open-Source Telegram Monitoring Tool
- 构建开源Telegram监控工具，引入民间组织反馈优化大模型分类性能
- 通过持续反馈循环，使模型适应新出现的反民主言论模式
- 适合关注网络治理与技术共治的非政府组织和研究者参考
民间组织在监测有害网络内容中的作用日益重要，尤其在平台方减少内容审核投入的背景下。人工智能工具可协助大规模识别有害内容，但现有开源工具极少实现AI模型与社交媒体监控系统的无缝集成。鉴于民间组织对有害内容具有主题专长和语境理解力，应作为技术工具共同开发的积极参与者，提供反馈以改进模型并确保符合利益相关方的需求与价值，而非仅作为被动使用者。然而，开源社区、学术界与民间组织的合作仍罕见，有害内容研究也少有转化为民间组织可用的实际工具。本文探讨如何让民间组织深度参与我们正在与民间组织合作开发的基于大模型的开源Telegram反民主运动监控工具，推动技术与社会需求的协同演化。
原文摘要 · Abstract (English)
The role of civil society organizations (CSOs) in monitoring harmful online content is increasingly crucial, especially as platform providers reduce their investment in content moderation. AI tools can assist in detecting and monitoring harmful content at scale. However, few open-source tools offer seamless integration of AI models and social media monitoring infrastructures. Given their thematic expertise and contextual understanding of harmful content, CSOs should be active partners in co-developing technological tools, providing feedback, helping to improve models, and ensuring alignment with stakeholder needs and values, rather than as passive 'consumers'. However, collaborations between the open source community, academia, and civil society remain rare, and research on harmful content seldom translates into practical tools usable by civil society actors. This work in progress explores how CSOs can be meaningfully involved in an AI-assisted open-source monitoring tool of anti-democratic movements on Telegram, which we are currently developing in collaboration with CSO stakeholders.
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