## 2 Background  
Air transport contributed more than \(\upcels110\) billion to the European GDP(gross domestic product) in 2014, and airlines have usually been effective in improving productivity [25]. The efficiency of the internal management of institutions is a function of the costs and benefits of their activities [26]. Literature has plenty of examples of the use of DEA to measure efficiency in airlines (see Table 1). This technique has often been utilized to evaluate efficiency through the use of financial resource indicators, both at corporate and at market level [27]. However, there exist two main trends in which airlines have been evaluated in the last years, in terms of performance: economic and/or operative [28; 29; 30; 31] and economic and/or environmental [7; 14; 32]. There have been a considerable number of studies measuring the efficiency of European institutions through DEA [14; 33; 34], between others. [PERSON] and [PERSON] [35] used DEA-CCR(Charnes-Cooper-Rhodes) and two-stage regression to analyze technical efficiency and concluded that airlines should consider managerial causes of inefficiency scores when developing strategies, whilst [PERSON] and [PERSON] [36] employed a double bootstrapping DEA model to analyze the period following deregulation of the European airline industry and financial turmoil in the US airline industry. Another study [14] used network slacks-based measures to analyze how including aviation in the European Union Emission Trading Scheme has affected airline efficiency since 2008. [PERSON] and [PERSON] [37] used two-stage DEA to measure the efficiency of 18 European public service obligation (PSO) airlines over two fiscal years and concluded that operators with a large number of PSO contracts appear to be more efficient than those with only a few such contracts. There are also studies of European airlines that have used DEA to explore environmental factors [34]; these studies suggest that the average productivity of airlines, which take an environmentally sensitive approach to growth, is lower than that of airlines taking a more traditional approach. More recent research [33] has used network-based DEA and shows a dynamic component, since performance differed across types of airlines during the 2000s. However, in the above works, although the methodology applied to obtain conclusions is based on DEA, and they have brought about some important advances in gaining a better understanding of the functioning of the airline industry, they have not addressed airlines in Europe as an ecosystem, where other factors may be considered. As an example, in [38], the authors present a review specific to European airlines and social media research, which concludes that new information and communication technology has had a profound impact on airlines' delivery and consumption, increasing customer engagement, and loyalty in a technologically connected world [32; 38; 39; 40; 41; 42]. With this in mind, in this paper, we add analysis of social media indicators to a DEA-based approach measurement of the efficiency of airlines. To understand the functioning of a complex system, it is essential to analyze its structure [43]. There is some research which has used network analysis to study the development of the airline industry. For instance, [PERSON] [44] presented a simple rule for the computation of airport congestion tolls, reflecting the internalization of congestion by analyzing a complex network formed by hub airports. [PERSON] [45] analyzed the Indian airport network (as a weighted network), concluding that it follows a small-world model and comparing it to the worldwide airport network. To the best of our knowledge, however, network analysis has not previously been used to estimate airline efficiency. In this paper, we propose to study the network formed by the airports in which the airlines operate. By analyzing such a network, we will be able to extract certain centrality measures that may provide useful information about the airline business positioning.