Zero-Training Temporal Drift Detection for Transformer Sentiment Models: A Comprehensive Analysis on Authentic Social Media Streams

TL;DR

This paper introduces a zero-training method for detecting temporal drift in transformer sentiment models on social media data, revealing significant instability during major events and proposing efficient metrics for real-time monitoring.

Contribution

It presents four novel drift metrics that outperform existing baselines and validates their effectiveness across multiple real-world social media events.

Findings

Model accuracy drops up to 23.4% during events

Maximum confidence drops of 13.0% with strong correlation to performance

Proposed metrics outperform embedding-based baselines

Abstract

We present a comprehensive zero-training temporal drift analysis of transformer-based sentiment models validated on authentic social media data from major real-world events. Through systematic evaluation across three transformer architectures and rigorous statistical validation on 12,279 authentic social media posts, we demonstrate significant model instability with accuracy drops reaching 23.4% during event-driven periods. Our analysis reveals maximum confidence drops of 13.0% (Bootstrap 95% CI: [9.1%, 16.5%]) with strong correlation to actual performance degradation. We introduce four novel drift metrics that outperform embedding-based baselines while maintaining computational efficiency suitable for production deployment. Statistical validation across multiple events confirms robust detection capabilities with practical significance exceeding industry monitoring thresholds. This zero-training methodology enables immediate deployment for real-time sentiment monitoring systems and provides new insights into transformer model behavior during dynamic content periods.