Civil Society in the Loop: Feedback-Driven Adaptation of (L)LM-Assisted Classification in an Open-Source Telegram Monitoring Tool

TL;DR

This paper discusses developing an open-source AI-assisted Telegram monitoring tool for civil society organizations, emphasizing collaborative feedback-driven adaptation to improve detection of harmful content related to anti-democratic movements.

Contribution

It introduces a novel participatory approach involving CSOs in co-developing and refining AI tools for monitoring harmful online content on Telegram.

Findings

Initial collaboration with CSOs enhances tool relevance and usability

Feedback-driven adaptation improves detection accuracy

Open-source integration facilitates broader civil society engagement

Abstract

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.