A probablistic model for spatio-temporal signal extraction from social media

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.