Robust Spatio-Temporal Signal Recovery from Noisy Counts in Social Media

TL;DR

This paper introduces a novel Poisson-based model for recovering accurate spatio-temporal signals from noisy social media counts, explicitly addressing biases, delays, and distortions, with demonstrated improvements over existing methods.

Contribution

It formulates signal recovery as a Poisson point process estimation, incorporating bias correction, regularization, and an efficient optimization algorithm, advancing prior approaches.

Findings

Model outperforms baseline methods in accuracy.

Effective handling of biases and distortions.

Qualitative success in wildlife roadkill case study.

Abstract

Many real-world phenomena can be represented by a spatio-temporal signal: where, when, and how much. Social media is a tantalizing data source for those who wish to monitor such signals. Unlike most prior work, we assume that the target phenomenon is known and we are given a method to count its occurrences in social media. However, counting is plagued by sample bias, incomplete data, and, paradoxically, data scarcity -- issues inadequately addressed by prior work. We formulate signal recovery as a Poisson point process estimation problem. We explicitly incorporate human population bias, time delays and spatial distortions, and spatio-temporal regularization into the model to address the noisy count issues. We present an efficient optimization algorithm and discuss its theoretical properties. We show that our model is more accurate than commonly-used baselines. Finally, we present a case study on wildlife roadkill monitoring, where our model produces qualitatively convincing results.