arXiv:2605.27911v1 Announce Type: new Abstract: Suicide is a critical global public health challenge, causing approximately 720,000 deaths each year and calling for timely, effective prevention strategies. Existing computational studies primarily focus on post-based social media platforms such as Twitter and Weibo, leaving instant messaging environments such as Telegram underexplored. Yet group chats pose distinct challenges: messages are short, fragmented, multi-party, and often rely on implicit or culturally specific expressions, making isolated post-level analysis insufficient. We introduce
The proliferation of instant messaging and advanced AI capabilities makes benchmarking contextual risk assessment in these environments both possible and crucial for public health.
This research addresses a critical gap in suicide prevention by developing AI models for complex, real-time communication, moving beyond established social media platforms.
The focus shifts from isolated post-level analysis on static platforms to dynamic, multi-party interactions in chat environments, potentially enabling earlier and more nuanced interventions.
- · Public health organizations
- · AI ethicists
- · NLP researchers
- · Mental health support services
- · Current static AI risk assessment models
- · Platforms lacking contextual AI analysis capabilities
Improved AI models for detecting distress in real-time, unstructured conversations.
Development of proactive intervention tools integrated directly into messaging platforms to support individuals at risk.
Enhanced public trust in AI for sensitive applications, leading to wider adoption in healthcare and social welfare contexts.
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Read at arXiv cs.AI