# ASSED -- A Framework for Identifying Physical Events through Adaptive Social Sensor Data Filtering

arXiv: https://arxiv.org/abs/1909.07596 · HTML: https://mlsanity.com/paper/1909.07596/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Sep 2019.

## Built on by (most cited first, 3 of 3)

- [Concept Drift Adaptation in Text Stream Mining Settings: A Systematic Review](https://mlsanity.com/paper/2312.02901/) (2023)
- [Methods for Generating Drift in Text Streams](https://mlsanity.com/paper/2403.12328/) (2024)
- [Improving Sampling Methods for Fine-tuning SentenceBERT in Text Streams](https://mlsanity.com/paper/2403.15455/) (2024)

## Abstract (arXiv)

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 textitconcept 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- textitand 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.