Abstract
In this paper, we introduce a novel way to model and analyze social media interactions by leveraging the proxemics theory. Proxemics is the science that studies the effect of space and distance on interactions and behaviors. It is generally applied to the physical space but we hypothesize that adapting it to social media could provide a generic way to model and analyze the various kinds of interactions taking place in this virtual space. We designed a proxemic-based framework aiming to guide the analysis of data from a social media corpus that can be contextualized to a given application domain. We start by formally redefining proxemics in the context of social media and we leverage this redefinition to design a generic and extensible proxemic-based trajectory model dedicated to social media. We also propose novel proxemic distances applicable to this model. Finally, we experiment this proxemic framework on the field of tourism. The application to this use case demonstrates our framework’s flexibility and effectiveness to model and analyze social media interactions.
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Masson, M., Roose, P., Sallaberry, C., Agerri, R., Bessagnet, MN., Lacayrelle, A.L.P. (2023). APs: A Proxemic Framework for Social Media Interactions Modeling and Analysis. In: Crémilleux, B., Hess, S., Nijssen, S. (eds) Advances in Intelligent Data Analysis XXI. IDA 2023. Lecture Notes in Computer Science, vol 13876. Springer, Cham. https://doi.org/10.1007/978-3-031-30047-9_23
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