Robust Spatio-Temporal Signal Recovery from Noisy Counts in Social Media
This work addresses the challenge of monitoring real-world phenomena like wildlife roadkill using social media data, which is incremental in improving signal recovery methods for domain-specific applications.
The paper tackled the problem of recovering spatio-temporal signals from noisy social media counts by formulating it as a Poisson point process estimation, incorporating biases and distortions, and demonstrated improved accuracy over baselines with a case study on wildlife roadkill monitoring.
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.