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The present work aims to study and model the integration of refugees who appear in a society, through the implementation of adaptive social networks. In our model, for each individual, their characteristics of religiousness and language skills are considered for the social integration. We also explore the homophily phenomenon as part of the creation of new connections and the changes on the refugees’ traits. As it most commonly happens in real life, refugees appear in a community-structured social network as individual nodes without any connection, and interactions between refugees and non-refugees are built through a defined methodology which applies local search, random attachment and node deletion. We show a few case scenarios and perform a social network analysis.

The task of classifying political tweets has been shown to be very difficult, with controversial results in many works and with non-replicable methods. Most of the works with this goal use rule-based methods to identify political tweets. We propose here two methods, being one rule-based approach, which has an accuracy of 62%, and a supervised learning approach, which went up to 97% of accuracy in the task of distinguishing political and non-political tweets in a corpus of 2.881 Dutch tweets. Here we show that for a data base of Dutch tweets, we can outperform the rule-based method by combining many different supervised learning methods.

Item does not contain fulltext The reduction of childhood obesity through the promotion of a healthy lifestyle is one of the most important public health challenges at the moment. It is known that the unhealthy habits of children can cause unavoidable side effects in their early stage of life, including both physical and mental consequences. This work considers that the physical activity level of children is a behaviour that can be spread throughout the social relations of children in their daily life at school. Therefore, the aim of this work is to define what the best strategy is to find 'targets' (i.e., influential children that can initiate behavioural change) for physical activity (PA) interventions that would affect the average PA of a population of Dutch school classes. We tuned a model based on the influence of the children’s peers in their social network, based on the data set from the MyMovez project - Phase I. Five intervention strategies were implemented, and their efficacy was compared. Once the targets were chosen, an increase of 17% was applied to their initial PA. Then, the diffusion model was run to verify the improvement on the PA of the whole network after one year. We discuss implications of the simulation results on which strategies may be used to make informed choices about the setup of social network interventions and future model improvements. Our results show that targeting more vulnerable children (i.e. in a worse environment) and applying a network optimization algorithm are the best solutions for this data set indicating that future interventions should aim for these two strategies. 8th International Conference on Simulation and Modeling Methodologies, Technologies and Applications (SIMULTECH 2018), Porto, Portugal , 29-31 July 2018