It is nowadays possible to access a huge and increasing stream of social media records. Recently, such data has been used to infer about spatio-temporal phenomena by treating the records as proxy observations of the real world. However, since such observations are heavily uncertain and their spatio-temporal distribution is highly heterogeneous, extracting meaningful signals from such data is a challenging task. In this paper, we present a probabilistic model to extract spatio-temporal distributions of phenomena (called spatio-temporal signals) from social media. Our approach models spatio-temporal and semantic knowledge about real-world phenomena embedded in records on the basis of conditional probability distributions in a Bayesian network. Through this, we realize a generic and comprehensive model where knowledge and uncertainties about spatio-temporal phenomena can be described in a modular and extensible fashion. We show that existing models for the extraction of spatio-temporal phenomena distributions from social media are particular instances of our model. We quantitatively evaluate instances of our model by comparing the spatio-temporal distributions of extracted phenomena from a large Twitter data set to their real-world distributions. The results clearly show that our model allows to extract better spatio-temporal signals in terms of quality and robustness.

العنوان

Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

الناشر

ACM

بلد الناشر

الولايات المتحدة الأمريكية

نوع المنشور

مطبوع فقط

المجلد

--

السنة

2013

الصفحات

10