Concept Drift Adaptive Physical Event Detection for Social Media Streams

TL;DR

This paper introduces an adaptive event detection system for social media streams that effectively handles concept drift, improving detection accuracy and sensitivity for both weak and strong signals without human intervention.

Contribution

It presents a novel, continuously updating machine learning approach for physical event detection in social media, addressing concept drift and integrating heterogeneous data sources.

Findings

Detects nearly 350% more landslides than static methods.

Achieves event detection accuracy of 0.988 with adaptive classifiers.

Outperforms static approaches with an accuracy of 0.762.

Abstract

Event detection has long been the domain of physical sensors operating in a static dataset assumption. The prevalence of social media and web access has led to the emergence of social, or human sensors who report on events globally. This warrants development of event detectors that can take advantage of the truly dense and high spatial and temporal resolution data provided by more than 3 billion social users. The phenomenon of concept drift, which causes terms and signals associated with a topic to change over time, renders static machine learning ineffective. Towards this end, we present an application for physical event detection on social sensors that improves traditional physical event detection with concept drift adaptation. Our approach continuously updates its machine learning classifiers automatically, without the need for human intervention. It integrates data from heterogeneous sources and is designed to handle weak-signal events (landslides, wildfires) with around ten posts per event in addition to large-signal events (hurricanes, earthquakes) with hundreds of thousands of posts per event. We demonstrate a landslide detector on our application that detects almost 350% more land-slides compared to static approaches. Our application has high performance: using classifiers trained in 2014, achieving event detection accuracy of 0.988, compared to 0.762 for static approaches.