Abstract
Discovering interesting locations to users is a challenge for social and productive networks. The evidence of the content produced by users must be considered in this task, which may be simplified by the use of the metadata associated with the content, i.e., the categorization supported by the network, namely – descriptive keywords and geographic coordinates. In this book chapter we present a productive network representation model, designed to discover indirect keywords and locations. The spatial dimension of the model enables indirect location discovery methods through the interpretation of the network as a graph, solely relying on keywords and locations that categorize or describe productive items. The model and indirect location discovery methodology presented in this chapter avoid content analysis, and are a new step towards a generic approach to the identification of relevant information, otherwise hidden from the users. The evaluation of the model and methods is accomplished by an experiment that performs a classification analysis over the Twitter network.
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Sabino, A., Rodrigues, A. (2017). Productive Networks and Indirect Locations. In: Leitner, M., Jokar Arsanjani, J. (eds) Citizen Empowered Mapping. Geotechnologies and the Environment, vol 18. Springer, Cham. https://doi.org/10.1007/978-3-319-51629-5_5
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