Abstract
Social Media has been disrupting traditional technology mediated learning, providing students and educators with unsupervised and informal tools and spaces where authentic learning occurs. Still, the traditional LMS persists as the core element in this context, while lacking additional management, monitoring and analysis tools to handle informal learning and content. In this paper, we present an integrated methodology that combines social network analytics, sentiment analysis and topic categorization to perform social content visualizations and analysis aimed at integrated learning environments. Results provide insights on networked content dimension, type of structure, degree of popularity and degree of controversy, as well as on their educational and functional potential in the field of learning analytics.

Abstract
Social media networks' popularity has been growing in almost every context of daily human interaction. Particularly concerning the education field, organisations and teachers have been continuously recognizing social media as a rich environment with potential to benefit the teaching-learning process, classroom administration and social interactions. However, social media networks have been used as complementary environments to the mandatory adoption of an institutional LMS, leading to the development of fragmented teaching-learning environments, where mutual interchanges are not consolidated nor allow for an explicit academic legitimacy, computation and management. Also, social media networks' integration in education has been viewed as an ad-hoc initiative of some educators, who are prone to incorporate web trends in their pedagogic activity, which are evaluated, most of the times, under the lens of recreational initiates. Consequently, there is an urgent need to bridge between the consolidated adoption of LMSs and the integration of social media networks in education, not only in terms of technological infrastructure ( interface and usability) but also in terms of production and management of its pedagogical outcomes. With the intent of providing solutions for the above context, in this paper, we discuss the concept of Social Student Relationship Management and present the EduBridge system, its current stage of development and highlight the educational applicability of a thorough set of social network analysis.

Abstract
The integration of social media in education has been raising new challenges for teachers, students and organizations, in both traditional and technology-mediated learnings settings. Formal higher education contexts are still mostly anchored and locked up in institutional LMS, despite the innumerous educational digressions that educators have been conducting throughout social media networks. One of the biggest challenges in contemporary educational needs consists on managing the integration, validation and reporting on educational processes, goals and student performance, when they are widely spread in several formal and informal contexts. In this chapter a system for the integration of LMS and social media is presented, as well as evidence on its practical usage. A set of social network analytics are also brought forward as features that are currently being added to the referred system.

Abstract
The authenticity of Information has become a longstanding issue affecting businesses and society, both for printed and digital media. On social networks, the reach and effects of information spread occur at such a fast pace and so amplified that distorted, inaccurate or false information acquires a tremendous potential to cause real world impacts, within minutes, for millions of users. Recently, several public concerns about this problem and some approaches to mitigate the problem were expressed. In this paper, we discuss the problem by presenting the proposals into categories: content based, source based and diffusion based. We describe two opposite approaches and propose an algorithmic solution that synthesizes the main concerns. We conclude the paper by raising awareness about concerns and opportunities for businesses that are currently on the quest to help automatically detecting fake news by providing web services, but who will most certainly, on the long term, profit from their massive usage. (C) 2017 The Authors. Published by Elsevier B.V.

Abstract
In this paper we analyze and discuss two methods that are based on the traditional k-means for document clustering and that feature integration of social tags in the process. The first one allows the integration of tags directly into a Vector Space Model, and the second one proposes the integration of tags in order to select the initial seeds. We created a predictive model for the impact of the tags' integration in both models, and compared the two methods using the traditional k-means++ and the novel k-C algorithm. To compare the results, we propose a new internal measure, allowing the computation of the cluster compactness. The experimental results indicate that the careful selection of seeds on the k-C algorithm present better results to those obtained with the k-means++, with and without integration of tags.

Abstract
In this paper we study the combined use of four different NLP toolkits-Stanford CoreNLP, GATE, OpenNLP and Twitter NLP tools-in the context of social media posts. Previous studies have shown performance comparisons between these tools, both on news and social media corporas. In this paper, we go further by trying to understand how differently these toolkits predict Named Entities, in terms of their precision and recall for three different entity types, and how they can complement each other in this task in order to achieve a combined performance superior to each individual one. Experiments on two publicly available datasets from the workshops WNUT-2015 and #MSM2013 show that using an ensemble of toolkits can improve the recognition of specific entity types - up to 10.62% for the entity type Person, 1.97% for the type Location and 1.31% for the type Organization, depending on the dataset and the criteria used for the voting. Our results also showed improvements of 3.76% and 1.69%, in each dataset respectively, on the average performance of the three entity types.