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This study investigates the challenges of interpreting harmful online communication within cybercrime communities on Discord, highlighting the role of slang and coded language. By constructing reference interpretations and evaluating both human and large language model (LLM) interpretations across varying context conditions, the authors reveal that local context is often inadequate for accurate understanding. Key findings indicate that while extended conversational context significantly enhances human interpretation, LLMs also benefit from local context, with larger models yielding better results, prompting a shift in how harmful-content analysis should be approached.
Interpretation of harmful online communication is not just about decoding messages; it requires integrating contextual knowledge, revealing significant gaps in both human and LLM understanding.
Harmful online communication often contains slang, coded terms, abbreviations, and community-specific expressions, which make messages difficult to interpret. This paper presents an exploratory study of interpretation difficulty in Discord chats related to cybercrime. We construct reference interpretations of purposefully selected difficult messages, which were reviewed by an expert. We then use them to evaluate human and large language model (LLM) interpretations under different context conditions. The results show that local context alone is often insufficient for humans, while external knowledge and extended conversational context substantially improve human interpretation. For LLMs, local context also improves interpretation, and the larger model performs better. We further conduct a qualitative error analysis and propose a preliminary classification of factors that make harmful chats difficult to interpret. These findings suggest that harmful-content analysis should treat interpretation as an evidence-integration problem, rather than as message-level classification alone.
