Interpreting harmful Discord messages requires integrating external knowledge and extended context, not just local message-level classification; LLMs leverage local context better than humans but still fail on coded language and community-specific references.
Psychological Bulletin , volume =
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
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cs.CL 2years
2026 2representative citing papers
EmoS is a new high-fidelity benchmark for fine-grained streaming emotional understanding that produces measurable gains when used to fine-tune multimodal large language models.
citing papers explorer
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Understanding Interpretation Difficulty in Harmful Online Communication: Insights from Cybercrime Communities
Interpreting harmful Discord messages requires integrating external knowledge and extended context, not just local message-level classification; LLMs leverage local context better than humans but still fail on coded language and community-specific references.
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EmoS: A High-Fidelity Multimodal Benchmark for Fine-grained Streaming Emotional Understanding
EmoS is a new high-fidelity benchmark for fine-grained streaming emotional understanding that produces measurable gains when used to fine-tune multimodal large language models.