Hot19net New [patched]

The air in the "Hot19Net" server room didn't just smell like ozone; it smelled like the future.

Hot19Net New

Abstract

Hot19Net New is a proposed architecture and pipeline for real-time detection and categorization of emergent events in social media streams. The system combines lightweight transformer encoders with temporal graph neural networks and an adaptive sampling mechanism to maximize detection recall while maintaining low latency and computational cost. We evaluate Hot19Net New on multi-platform datasets collected from Twitter, Reddit, and public news feeds, showing improvements in event detection F1 (+6.8%) and detection latency (−22%) compared to baseline streaming-event models. We also analyze robustness to noisy labels and concept drift, and discuss deployment considerations for privacy-preserving, resource-constrained environments. hot19net new

• Continual learning with reservoir sampling of recent data to fine-tune the encoder (low learning rate).
• Model drift detection triggers more aggressive retraining or parameter freeze.
• Semi-supervised label propagation: use high-confidence cluster labels to expand training set.

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