El texto completo de este trabajo no está disponible en el Repositorio Académico UPC por restricciones de la casa editorial donde ha sido publicado. / Background: The purpose of this study was to determine the association between social media dependence and depressive symptoms and also, to characterize the level of dependence. It was a transversal, analytical research. Subjects and Methods: The stratified sample was 212 students from a private university that used Facebook, Instagram and/or Twitter. To measure depressive symptoms, Beck Depression Inventory was used, and to measure the dependence to social media, the Social Media Addiction Test was used, adapted from the Internet Addiction Test of Echeburúa. The collected data were subjected for analysis by descriptive statistics where STATA12 was used. Results: The results show that there is an association between social media dependence and depressive symptoms (PR [Prevalence Ratio] = 2.87, CI [Confidence Interval] 2.03–4.07). It was also shown that preferring the use of Twitter (PR = 1.84, CI 1.21–2.82) over Instagram (PR = 1.61, CI 1.13–2.28) is associated with depressive symptoms when compared to the use of Facebook. Conclusion: Excessive social media use is associated with depressive symptoms in university students, being more prominent in those who prefer the use of Twitter over Facebook and Instagram. / Revisión por pares

The study of network-service disruptions caused by large-scale disturbances has mainly focused on assessing network damage; however, network-disruption responses, i.e., how the disruptions occur depending on social organizations, weather, and power resources, have been studied little. The goal of this research is to study the responses of network-service disruptions caused by large-scale disturbances with respect to (1) temporal and logical network, and (2) external factors such as weather and power resources, using real and publicly available heterogeneous data that are composed of network measurements, user inputs, organizations, geographic locations, weather, and power outage reports. Network-service disruptions at the subnet level caused by Hurricanes Katrina in 2005 and Ike in 2008 are used as the case studies. The analysis of network-disruption responses with respect to temporal and logical network shows that subnets became unreachable dependently within organization, cross organization, and cross autonomous system. Thus, temporal dependence also illustrates the characteristics of logical dependence. In addition, subnet unreachability is analyzed with respect to the external factors. It is found that subnet unreachability and the storm are weakly correlated. The weak correlation motivates us to search for root causes and discover that the majority of subnet unreachability reportedly occurred because of power outages or lack of power generators. Using the power outage data, it is found that subnet unreachability and power outages are strongly correlated.