Hot19net New File

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.

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