Abstract
Organisations have been striving to account for the resources they've been allocating to Social Media integration and management, essentially because this integration has been occurring without a previously designed content strategy, which will foster the desired fan engagement. In order to establish a comparison of social media strategies between HEIs, we developed a seven category model, encompassing the fundamental communication areas of focus for higher education service providers. Then, we performed a classification of these HEI posts in Facebook, according to our model. For this step, we used six of the most promising, and prominent, classifiers to obtain a predicted category for each post. Combining all posts from each HEI according to the model we get the HEI's editorial strategy. By clustering the overall social media strategies and corresponding response rate we discover the sector's monitoring HEI and, through a benchmarking process, we retrieve useful inputs for the design of social media strategies for HEI.

Abstract
The exponential growth of social media usage and the integration of digital natives in Higher Education Institutions (HEI) have been posing new challenges to both traditional and technology-mediated learning environments. Nowadays social media plays an important, if not central, role in society, for professional and personal purposes. However, it's important to highlight that in the mind of a digital native, social media is not just a tool, it is a place that is as real and as natural as any real-life world place where formal/informal social interactions happen. Still, formal higher education contexts are still mostly imprisoned in locked up institutional Learning Management Systems (LMS), while a new world of social connections grows and develops itself outside schools. One of the main reasons we believe to be persisting in the origin of the matter is the absence of a suitable management, monitoring and analysis tools to legitimize and to efficiently manage the relationship with students in social networks. In this paper we discuss the growing relevance of the "Social Student Relationship Management" concept and introduce the EduBridge Social system, which aims at connecting the most commonly used LMS, Moodle, and the most popular social network, Facebook.

Abstract
In sentiment analysis the polarity of a text is often assessed recurring to sentiment lexicons, which usually consist of verbs and adjectives with an associated positive or negative value. However, in short informal texts like tweets or web comments, the absence of such words does not necessarily indicates that the text lacks opinion. Tweets like "First Paris, now Brussels... What can we do?" imply opinion in spite of not using words present in sentiment lexicons, but rather due to the general sentiment or public opinion associated with terms in a specific time and domain. In order to complement general sentiment dictionaries with those domain and time specific terms, we propose a novel system for lexicon expansion that automatically extracts the more relevant and up to date terms on several different domains and then assesses their sentiment through Twitter. Experimental results on our system show an 82% accuracy on extracting domain and time specific terms and 80% on correct polarity assessment. The achieved results provide evidence that our lexicon expansion system can extract and determined the sentiment of terms for domain and time specific corpora in a fully automatic form.

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
In this paper we introduce three main features extracted from Moodle logs in order to be uses a possible means to predict future student grades. We discuss the statistical analysis on these features and show how they cannot be applied isolatedly to model our data. We then apply them as a whole and use principal component analysis to derive a decision tree based on the features. With derived tree we are able to predict grades in three intervals, namely to predict failures. Our proposed analysis methodology can be incorporated in an LMS and be used during a course. As the course unfolds, the system can to trigger alarms regarding possible failure situations.

Abstract
The ability to handle large amounts of unstructured information, to optimize strategic business opportunities, and to identify fundamental lessons among competitors through benchmarking, are essential skills of every business sector. Currently, there are dozens of social media analytics' applications aiming at providing organizations with informed decision making tools. However, these applications rely on providing quantitative information, rather than qualitative information that is relevant and intelligible for managers. In order to address these aspects, we propose a semi-supervised learning procedure that discovers and compiles information taken from online social media, organizing it in a scheme that can be strategically relevant. We illustrate our procedure using a case study where we collected and analysed the social media discourse of 43 organizations operating on the Higher Public Polytechnic Education Sector. During the analysis we created an "editorial model" that characterizes the posts in the area. We describe in detail the training and the execution of an ensemble of classifying algorithms. In this study we focus on the techniques used to increase the accuracy and stability of the classifiers.