Abstract
Online social networks present themselves as valuable information sources about their users and their respective behaviours and interests. Many researchers in data mining have analysed these types of data, aiming to find interesting patterns. This paper addresses the problem of identifying and displaying tweet profiles by analysing multiple types of data: spatial, temporal, social and content. The data mining process that extracts the patterns is composed by the manipulation of the dissimilarity matrices for each type of data, which are fed to a clustering algorithm to obtain the desired patterns. This paper studies appropriate distance functions for the different types of data, the normalization and combination methods available for different dimensions and the existing clustering algorithms. The visualization platform is designed for a dynamic and intuitive usage, aimed at revealing the extracted profiles in an understandable and interactive manner. In order to accomplish this, various visualization patterns were studied and widgets were chosen to better represent the information. The use of the project is illustrated with data from the Portuguese twittosphere.
An earlier version of this work was presented at the Encontro Nacional de Inteligência Artificial e Computacional (Brazilian AI meeting - ENIAC).
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Cunha, T., Soares, C., Mendes Rodrigues, E. (2014). TweeProfiles: Detection of Spatio-temporal Patterns on Twitter. In: Luo, X., Yu, J.X., Li, Z. (eds) Advanced Data Mining and Applications. ADMA 2014. Lecture Notes in Computer Science(), vol 8933. Springer, Cham. https://doi.org/10.1007/978-3-319-14717-8_10
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DOI: https://doi.org/10.1007/978-3-319-14717-8_10
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