Abstract
The organizational impacts of adopting social media have been on the top key concerns of organizations entering these environments. Organizations are, in fact, allocating time, effort, skills, human resources and technology and this raises the constant need to measure the ROI and legitimize the use of social media in the context of organizational development. However, how can organizations attempt to measure the efficiency and return on investments on a social media content approach that has not been strategically designed? In this paper, we report on previous research which we have further developed into a more comprehensive and solid analysis of types of social media content strategies that are being implemented in the Higher Education Sector, using clustering to group analogue content strategies and social media KPI to measure the efficiency of each of the main i. This work is based on a previously proposed editorial model for the design of social media content strategies for Higher Education Institutions, and results show which are the most relevant strategic areas of communication and corresponding return, in terms of publics' engagement, that organizations can obtain. (C) 2017 The Authors. Published by Elsevier B.V.

Abstract
Social networks can often be considered the main stage of news, so detecting newsworthy information in this media is a relevant subject of study. Labeling automatically messages shared in social networks is an area of study that can be used directly to detect newsworthy information or to serve as training data for other projects. The solution presented in this work is to use the news as the base knowledge for the classification of messages. The results of this application were promising, with an accuracy of over 90% in detecting news related messages in our datasets. © 2017 IEEE.

Abstract
Measuring the return on communication investments on social media has become one of the top key issues for organizations joining social networks. However, this field has been lacking articulation between what is conveyed as social media key performance indicators and the alignment of strategic organizational goals. Therefore, we propose a methodology to measure the performance of each organization on social media, to determine their positioning in the sector and to evaluate which are the content strategies used to boost the highest performing organizations. Thus, we identify how to determine which organizations should be closely monitored within the sector and which type content strategies can foster higher organizational performance on social media. © 2017 Copyright is held by the owner/author(s).

Abstract
Social networks as Facebook and Twitter gained a remarkable attention in the last decade. A huge amount of data is emerging and posted everyday by users that are becoming more interested in and relying on social network for information, news and opinions. Real time posting came to rise and turned easier to report news and events. However, due to its dimensions, in this work we focus on building a system architecture capable of detecting journalistic relevance of posts automatically on this 'haystack' full of data. More specifically, users will have the change to interact with a 'friendly user interface' which will provide several tools to analyze data. © 2017.

Abstract
The structural patterns in the neighborhood of nodes assign unique roles to the nodes. Mining the set of existing roles in a network provides a descriptive profile of the network and draws its general picture. This paper proposes a new method to determine structural roles in a dynamic network based on the current position of nodes and their historic behavior. We develop a temporal ensemble clustering technique to dynamically find groups of nodes, holding similar tempo-structural roles. We compare two weighting functions, based on age and distribution of data, to incorporate temporal behavior of nodes in the role discovery. To evaluate the performance of the proposed method, we assess the results from two points of view: 1) goodness of fit to current structure of the network; 2) consistency with historic data. We conduct the evaluation using different ensemble clustering techniques. The results on real world networks demonstrate that our method can detect tempo-structural roles that simultaneously depict the topology of a network and reflect its dynamics with high accuracy.

Abstract
We address the problem of whether networks of mobile devices such as smart-phones or tablets can be used to perform opportunistic, best-effort, parallel computations. We designed and implemented P3-Mobile, a parallel programming system for edge-clouds of Android devices to test the feasibility of this idea. P3-Mobile comes with a programming model that supports parallel computations over peer-to-peer overlays mapped onto mobile networks. The system performs automatic load-balancing by using the overlay to discover work. We present preliminary performance results for a parallel benchmark, using up to 16 devices, and discuss their implications towards future work. Copyright © 2017 ACM.