Abstract
With the increased use of machine learning algorithms to make decisions which impact people's lives, it is of extreme importance to ensure that predictions do not prejudice subgroups of the population with respect to sensitive attributes such as race or gender. Discrimination occurs when the probability of a positive outcome changes across privileged and unprivileged groups defined by the sensitive attributes. It has been shown that this bias can be originated from imbalanced data contexts where one of the classes contains a much smaller number of instances than the other classes. It is also important to identify the nature of the imbalanced data, including the characteristics of the minority classes' distribution. This paper presents FAWOS: a Fairness-Aware oversampling algorithm which aims to attenuate unfair treatment by handling sensitive attributes' imbalance. We categorize different types of datapoints according to their local neighbourhood with respect to the sensitive attributes, identifying which are more difficult to learn by the classifiers. In order to balance the dataset, FAWOS oversamples the training data by creating new synthetic datapoints using the different types of datapoints identified. We test the impact of FAWOS on different learning classifiers and analyze which can better handle sensitive attribute imbalance. Empirically, we observe that this algorithm can effectively increase the fairness results of the classifiers while not neglecting the classification performance. Source code can be found at: https://github.com/teresalazar13/FAWOS

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Retirees have been growing in importance as a consumer segment targeted by the tourism industry, namely because they can minimize the typical seasonality of tourism and increase its sustainability. This study aims to contribute to a more in-depth knowledge of retirees' behaviour and has two objectives: (i) describe tourist behaviour of seniors prior to and after retirement; (ii) identify and analyse retired consumers' current motivations and constraints towards tourism. Qualitative research was conducted comprising interviews with 40 Portuguese retirees. The results indicate a diversity of experiences regarding tourism activities both before and after retirement, evidencing that past experience stands out as a determinant of retirees' tourism behaviour. Moreover, three distinct segments of tourists emerge: the experts, the new tourists, and the non-tourists.

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The study presented in this article aims at understanding the relevance of mobility initiatives to the internationalisation efforts of Higher Education Institutions (HEIs). By building upon relevant literature, 17 propositions related to this contribution were identified. Empirical evidence from a concrete case of a European university was then used to evaluate those propositions. Data was collected from individual interviews to 19 outgoing faculty and from focus groups with 32 incoming students, resulting in the identification of the promises and pitfalls of mobility. The study concludes that HEIs must define clear strategies and carefully manage their mobility activities to maximise the potential benefits for internationalisation. Based on this main implication, it presents a set of managerial recommendations that may be relevant for those involved in administering or promoting international mobility programmes at universities, governments or international organisations, and for researchers in higher education.

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Starting from a gap identified in the literature regarding the use of social networks by newspapers to disseminate urgent news, this article aims to study strategies of journalistic content in social media, particularly in the context of a public crisis and to compare the effectiveness of different types of news disseminated in this medium, namely in terms of reach and generated interaction. The following research question was defined: how popular was public health news in Brazil during the covid-19 pandemic? Based on contributions in the literature, a quantitative study was carried out, using the content analysis technique. The study enable to better understand the sharing behavior of news in Twitter, the consumption behavior of newspaper readers on social networks and the generation of news during the pandemic.