Socioscope: spatio-temporal signal recovery from social media (extended abstract)

Authors:
Jun-Ming Xu;Aniruddha Bhargava;Robert Nowak;Xiaojin Zhu
Affiliations:
Department of Computer Sciences, University of Wisconsin-Madison, Madison, WI;Department of Electrical and Computer Engineering, University of Wisconsin-Madison, Madison, WI;Department of Electrical and Computer Engineering, University of Wisconsin-Madison, Madison, WI;Department of Computer Sciences, University of Wisconsin-Madison, Madison, WI and Department of Electrical and Computer Engineering
Venue:
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Year:
2013

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Abstract

Counting the number of social media posts on a target phenomenon has become a popular method to monitor a spatiotemporal signal. However, such counting is plagued by biased, missing, or scarce data. We address these issues by formulating signal recovery as a Poisson point process estimation problem. We explicitly incorporate human population bias, time delays and spatial distortions, and spatiotemporal regularization into the model to address the data quality issues. Our model produces qualitatively convincing results in a case study on wildlife roadkill monitoring.