{"type":"ScholarlyArticle","title":"A Density-based Nonparametric Model for Online Event Discovery from the Social Media Data","authors":[{"name":"Jinjin Guo","slug":"jinjin-guo","given":"Jinjin","family":"Guo","affiliation":"Department of Computer and Information Science , University of Macau , Macau SAR { 1 yb57414 ,"},{"name":"Zhiguo Gong","slug":"zhiguo-gong","given":"Zhiguo","family":"Gong","affiliation":"Department of Computer and Information Science , University of Macau , Macau SAR { 1 yb57414 ,"}],"abstract":"In this paper, we propose a novel online event discovery model DP-density to capture various events from the social media data. The proposed model can flexibly accommodate the incremental arriving of the social documents in an online manner by leveraging Dirichlet Process, and a density based technique is exploited to deduce the temporal dynamics of events. The spatial patterns of events are also incorporated in the model by a mixture of Gaussians. To remove the bias caused by the streaming process of the documents, Sequential Monte Carlo is used for the parameter inference. Our extensive experiments over two different real datasets show that the proposed model is capable to extract interpretable events effectively in terms of perplexity and coherence.","doi":"10.24963/ijcai.2017/240","url":"https://ijcai.org/proceedings/2017/240","pdf":"https://ijcai.org/proceedings/2017/0240.pdf","markdown":"https://ijcai.org/proceedings/2017/240.md","bibtex":"https://ijcai.org/proceedings/2017/bibtex/240","volume":{"slug":"2017","title":"Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2017","url":"https://ijcai.org/proceedings/2017/","year":2017,"location":"Melbourne, Australia","dates":"19-25 August 2017","editors":"Carles Sierra","isbn":"978-0-9992411-0-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2017"},"section":"Machine Learning A-R","track":"Main track","number":240,"pages":{"first":1732,"last":1738},"keywords":["Machine Learning: Time-series/Data Streams","Machine Learning: Learning Graphical Models","Uncertainty in AI: Approximate Probabilistic Inference"],"videos":[],"references":[{"position":1,"raw":"[Ahmed and Xing, 2008] Amr Ahmed and Eric P. 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