{"type":"ScholarlyArticle","title":"Socioscope: Spatio-Temporal Signal Recovery from Social Media (Extended Abstract)","authors":[{"name":"Jun-Ming Xu","slug":"jun-ming-xu","given":"Jun-Ming","family":"Xu","affiliation":""},{"name":"Aniruddha Bhargava","slug":"aniruddha-bhargava","given":"Aniruddha","family":"Bhargava","affiliation":""},{"name":"Robert D. Nowak","slug":"robert-d-nowak","given":"Robert D.","family":"Nowak","affiliation":""},{"name":"Xiaojin Zhu","slug":"xiaojin-zhu","given":"Xiaojin","family":"Zhu","affiliation":""}],"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.","doi":null,"url":"https://ijcai.org/proceedings/2013/464","pdf":"https://ijcai.org/proceedings/2013/0464.pdf","markdown":"https://ijcai.org/proceedings/2013/464.md","bibtex":"https://ijcai.org/proceedings/2013/bibtex/464","volume":{"slug":"2013","title":"Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2013","url":"https://ijcai.org/proceedings/2013/","year":2013,"location":"Beijing, China","dates":"3–9 August 2013","editors":"Francesca Rossi","isbn":"978-1-57735-633-2","publisher":"AAAI Press / International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":null,"track":"","number":464,"pages":{"first":3096,"last":3099},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}