Ctrl-F-Resist：民间社会组织监控网络极右翼的实践、挑战与技术需求
Ctrl-F-Resist. Practices, Challenges, and Technical Needs of Civil Society Organizations Monitoring the Far-Right Online
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中文总结 AI 辅助
本文通过对德国12家民间社会组织15名从业者的定性研究，分析其监控网络极右翼的实践与挑战，提出需开发定制化工具，引入开源Telegram监控原型，以破解“人工劳动陷阱”。
中文摘要 AI 辅助
随着极右翼势力越来越多地利用在线平台传播意识形态并动员支持者，民间社会组织（CSOs）在监控网络上的反民主动态方面发挥着至关重要但未得到充分认可的作用。与事实核查员或内容审核员不同，CSOs开展长期的、基于语境的分析，且往往处于资源有限和不稳定的环境中。尽管它们发挥着关键的社会作用，但在采用或共同开发技术解决方案方面面临重大障碍，包括法律不确定性、平台访问受限以及长期资金不足。现有研究和工具开发工作大多忽视了这些行为者，而更关注制度上更成熟的利益相关者。本文通过对来自12家德国民间社会组织的15名从事在线监控工作的从业者进行定性研究，解决了这一差距，将他们定位为数字空间治理中关键但被忽视的利益相关者。我们探讨了他们在技术支持方面的当前实践、挑战和期望。研究结果显示，由于缺乏定制化工具，监控工作在很大程度上仍依赖人工，而增强搜索能力是最紧迫的技术需求。尽管参与者对AI支持的功能（如媒体处理和内容发现）持开放态度，但许多人对自动分类仍持怀疑态度，理由是存在信任、法律可用性和专业可信度方面的担忧。基于这些发现，我们引入了一个概念性监控工作流，并描述了其在开源Telegram监控原型中的实现，该原型旨在灵活支持各种监控目标。我们概述了具体的设计、政策和研究建议，并引入了“人工劳动陷阱”这一基于实证的概念，解释了为何监控类CSOs往往会陷入劳动密集型、低能力的安排中。
英文摘要
As far-right actors increasingly exploit online platforms to disseminate ideology and mobilize supporters, civil society organizations (CSOs) play a vital yet underrecognized role in monitoring antidemocratic dynamics online. Unlike fact-checkers or content moderators, CSOs engage in long-term, contextualized analysis, often in resource-constrained settings and under precarious conditions. Despite their critical societal role, CSOs face significant barriers to adopting or co-developing technical solutions, including legal uncertainty, limited platform access, and chronic underfunding. Existing research and tool development efforts have largely overlooked these actors in favor of more institutionally embedded stakeholders. This paper addresses this gap through a qualitative study with 15 practitioners from 12 Germany-based CSOs engaged in online monitoring, positioning them as key yet overlooked stakeholders in the governance of digital spaces. We explore their current practices, challenges, and expectations regarding technological support. Our findings show that monitoring remains largely manual due to the lack of tailored tools, with enhanced search capabilities emerging as the most pressing technical need. While participants express openness to AI-supported features such as media processing and content discovery, many remain skeptical of automated classification, citing concerns around trust, legal usability, and professional credibility. Grounded in these findings, we introduce a conceptual monitoring workflow and describe its implementation in an open-source Telegram monitoring prototype designed to flexibly support diverse monitoring goals. We outline concrete design, policy, and research recommendatios, and introduce the manual labor trap as an empirically grounded concept that explains why monitoring CSOs tend to remain locked into labor-intensive, low-capacity arrangements.
发表机构
- Hochschule für Technik und Wirtschaft, University of Applied Science, Berlin（柏林应用技术大学（工程与经济应用技术大学））
- Technische Universität, Technical University, Berlin（柏林工业大学）
机构由 AI 辅助整理，请以论文原文为准。
