Socioscope: Spatio-Temporal Signal Recovery from Social Media (Extended Abstract)
Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence. Pages 3096-3099.
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.
@inproceedings{ijcai2013p464,
title = {Socioscope: Spatio-Temporal Signal Recovery from Social Media (Extended Abstract)},
author = {Xu, Jun-Ming and Bhargava, Aniruddha and Nowak, Robert D. and Zhu, Xiaojin},
booktitle = {Proceedings of the Twenty-Third International Joint Conference on
Artificial Intelligence, {IJCAI-13}},
publisher = {AAAI Press / International Joint Conferences on Artificial Intelligence},
editor = {Francesca Rossi},
pages = {3096--3099},
year = {2013},
month = {8},
url = {https://ijcai.org/proceedings/2013/464},
}
TY - CPAPER TI - Socioscope: Spatio-Temporal Signal Recovery from Social Media (Extended Abstract) AU - Xu, Jun-Ming AU - Bhargava, Aniruddha AU - Nowak, Robert D. AU - Zhu, Xiaojin T2 - Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence, IJCAI-13 PB - AAAI Press / International Joint Conferences on Artificial Intelligence PY - 2013 SP - 3096 EP - 3099 UR - https://ijcai.org/proceedings/2013/464 ER -Copied
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