Abstract
Social networks sounds to be a rich source to discover events mobility and analyzing their trends. Hence, the mobility of events refers to the movement of users’ opinions, location, velocity and the continuous change over time. Despite the ability of existing methods to deal with the event mobility and evolution. To the best of our knowledge, there is no research able to show the relation between mobility and social interactions. In this work, we associate mobility into event mining issue. We also describe the movement of opinions in social network and we aim at extracting useful information from tweeter posts, especially during the economic and political event “TUNISIA 2020”. To achieve this task, we focused on the use of machine learning techniques to analyze tunisian tweeter posts and classify their opinions temporally about this event for each Tunisian region. We introduced decision tree method to model and analyze event mobility and to predict the change of opinions from its spatial and temporal co-occurrence. Therefore, an entropy measure has been proposed based on spatio-temporal attributes as branching attributes. Finally, in order to validate our solution, we used real data and we performed some comparative experiments to show the effectiveness of our method.
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Toujani, R., Dhouioui, Z., Akaichi, J. (2018). Mobility Based Machine Learning Modeling for Event Mining in Social Networks. In: De Pietro, G., Gallo, L., Howlett, R., Jain, L. (eds) Intelligent Interactive Multimedia Systems and Services 2017. KES-IIMSS-18 2018. Smart Innovation, Systems and Technologies, vol 76. Springer, Cham. https://doi.org/10.1007/978-3-319-59480-4_31
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DOI: https://doi.org/10.1007/978-3-319-59480-4_31
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