Abstract
An overwhelming quantity of messages is posted in social networks every minute. To make the utilization of these platforms more productive, it is imperative to filter out information that is irrelevant to the general audience, such as private messages, personal opinions or well-known facts. This work is focused on the automatic classification of public social text according to its potential relevance, from a journalistic point of view, hopefully improving the overall experience of using a social network. Our experiments were based on a set of posts with several criteria, including the journalistic relevance, assessed by human judges. To predict the latter, we rely exclusively on linguistic features, extracted by Natural Language Processing tools, regardless the author of the message and its profile information. In our first approach, different classifiers and feature engineering methods were used to predict relevance directly from the selected features. In a second approach, relevance was predicted indirectly, based on an ensemble of classifiers for other key criteria when defining relevance-controversy, interestingness, meaningfulness, novelty, reliability and scope-also in the dataset. The first approach achieved a F (1)-score of 0.76 and an Area under the ROC curve (AUC) of 0.63. But the best results were achieved by the second approach, with the best learned model achieving a F (1)-score of 0.84 with an AUC of 0.78. This confirmed that journalistic relevance can indeed be predicted by the combination of the selected criteria, and that linguistic features can be exploited to classify the latter.

Abstract
We analysed the community structure of a network of news clips where relationships were established by the co-reference of entities in pairs of clips. Community detection was applied to a unidimensional version of the news clips network, as well as to a multidimensional version where dimensions were defined based on three different classes of entities: places, people, and dates. The goal was to study the impact on the quality of the identified community structure when using multiple dimensions to model the network. We did a two-fold evaluation, first based on the modularity metric and then based on human input regarding community semantics. We verified that the assessments of the evaluators differed from the results provided by the modularity metric, pointing towards the relevance of the utility and network integration phases in the identification of semantically cohesive groups of news clips. Copyright © 2013 Inderscience Enterprises Ltd.

Abstract
The use of Social Media applications in educational settings has gained attention ever since educators became aware of their growing role in student's daily routine. These arise as privileged tools for social interactions, information exchange, collaborative knowledge building, immediate communication and persistent attention retaining, among others. Consequently, these tools impose themselves as complements to the profoundly established use of the traditional LMS, either being propelled by educators or requested by students. In previous research, we have already identified Facebook groups as one of the social media applications with the highest potential to foster the development of social learning communities. We have acknowledged the need to integrate Facebook groups and corresponding learning analytics into formal learning environments, such as the institutional LMS, and we have developed and presented a system which performs that integration. However, as the educational settings diversify in terms of pedagogy, coursework and student's profile and cultural background, we have identified the need to extend this integration to other social media tools, such as the instant messaging app WhatsApp, and to provide valuable learning analytics on its usage. Mobile, instant messaging based learning communities differ a lot from forum-alike communities, where threads, topics, conversations and interactions are easily trackable and, for instance, social network analysis can be conducted to profile members, roles and relationships. Therefore, research presented in this paper adds to previous consolidated work both on the technological and analytical dimensions. We address the challenges posed by the integration of WhatsApp based learning analytics in the LMS Moodle, starting by the fact that, unlike Facebook groups, WhatsApp does not provide an API for developers, nor any stream of structured data that can feed a real-time monitoring system. We then focus research on revealing an actual set of visual learning analytics that characterize a learning community of about thirty foreign master students, who used WhatsApp as a complementary tool during a semester. We discuss which type of learning analytics and corresponding visualizations best suit WhatsApp learning communities; what can educators draw from the analytics of such communities; and how that information can strengthen student assessment and profiling.

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
Group work is an essential activity during both graduate and undergraduate formation. Students develop a set of skills, and employ criticism which helps them to better handle future interpersonal situations. There is a vast theoretical literature and numerous case studies about group work, but we haven't yet seen much development concerning the assessment of individual group participants. It is not always easy to have the perception of each student contribution to the whole work. Nevertheless, more than frequently, the assessment of the group is transposed to each group participant, which in turn results in each student having the same final mark. We propose and describe a tool to manage and assess individual group work taking into account the amount of work, interaction, quality, and the temporal evolution of each group participant. The module features the possibility to create two types of activities: collaborative or cooperative group work. We describe the conceptual design of our tool and present the two operating modes of the module, which is based on events, alerts and conditions. We then describe the methodology for the assessment in the two operating modes and how these two major approaches can be deployed through our module into pedagogical situations.

Abstract
The exploration of large networks carries inherent challenges in the visualization of a great amount of data. We built an interactive visualization system for the purpose of exploring a large multidimensional network of news clips over time. These are clips gathered by users from web news sources and references to people or places are extracted from. In this paper, we present the system's capabilities and user interface and discuss its advantages in terms of the browsing and extraction of knowledge from the data. These capabilities include a textual search and associated event detection, and temporal navigation allowing the user to seek a certain date and timespan.