  Physical event detection has long been the domain of static event processors
operating on numeric sensor data. This works well for large scale strong-signal
events such as hurricanes, and important classes of events such as earthquakes.
However, for a variety of domains there is insufficient sensor coverage, e.g.,
landslides, wildfires, and flooding. Social networks have provided massive
volume of data from billions of users, but data from these generic social
sensors contain much more noise than physical sensors. One of the most
difficult challenges presented by social sensors is \textit{concept drift},
where the terms associated with a phenomenon evolve and change over time,
rendering static machine learning (ML) classifiers less effective. To address
this problem, we develop the ASSED (Adaptive Social Sensor Event Detection)
framework with an ML-based event processing engine and show how it can perform
simple and complex physical event detection on strong- \textit{and} weak-signal
with low-latency, high scalability, and accurate coverage. Specifically, ASSED
is a framework to support continuous filter generation and updates with machine
learning using streaming data from high-confidence sources (physical and
annotated sensors) and social networks. We build ASSED to support procedures
for integrating high-confidence sources into social sensor event detection to
generate high-quality filters and to perform dynamic filter selection by
tracking its own performance. We demonstrate ASSED capabilities through a
landslide detection application that detects almost 350\% more landslides
compared to static approaches. More importantly, ASSED automates the handling
of concept drift: four years after initial data collection and classifier
training, ASSED achieves event detection accuracy of 0.988 (without expert
manual intervention), compared to 0.762 for static approaches.
