Abstract
Multimodal social networks are omnipresent in Web 2.0 with virtually every human communication action taking place there. Nonetheless, language remains by far the main premise such communicative acts unfold upon. Thus, it is statutory to discover language communities especially in social data stemming from historically multilingual countries such as Luxemburg. An adjacency tensor is especially suitable for representing such spatiosocial data. However, because of its potentially large size, heuristics should be developed for locating community structure efficiently. Linguistic structure discovery has a plethora of applications including digital marketing and online political campaigns, especially in case of prolonged and intense cross-linguistic contact. This conference paper presents TENSOR-G, a flexible genetic algorithm for approximate tensor clustering along with two alternative fitness functions derived from language variation or diffusion properties. The Kruskal tensor decomposition serves as a benchmark and the results obtained from a set of trilingual Luxemburgian tweets are analyzed with linguistic criteria.
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Acknowledgements
This conference paper has been developed within the framework of the project “Strengthening the Research Activities of the Directorate of the Greek School Network and Network Technologies”, financed by the own resources of the Computer Technology Institute and Press “Diophantos” (project code 0822/001).
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Drakopoulos, G., Stathopoulou, F., Tzimas, G., Paraskevas, M., Mylonas, P., Sioutas, S. (2017). A Genetic Algorithm for Discovering Linguistic Communities in Spatiosocial Tensors with an Application to Trilingual Luxemburg. In: Boracchi, G., Iliadis, L., Jayne, C., Likas, A. (eds) Engineering Applications of Neural Networks. EANN 2017. Communications in Computer and Information Science, vol 744. Springer, Cham. https://doi.org/10.1007/978-3-319-65172-9_53
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