Abstract
Organizations are rushing into social media networks following a worldwide trend to create a social presence in multiple media channels. However, a social media strategy needs to be aligned with and framed in the overall organizational strategic management goals. Higher Educational Institutions (HEI) are not different from other organizations in which concerns these problems. Determining the organizational positioning of an organization current strategy will allow to combine monitoring and benchmarking methods to foster the identification of opportunities and threats, which can serve as inputs for the internal evaluation of social media strategies', for the necessary strategic readjustments and a subsequent efficiency measurement. In order to address these challenges, we propose a three-step automatic data-mining procedure to assess the posting behavior and strategy of HEI, understand the editorial policy behind it, and predict the future HEI engagement. We used a sample of the 5-top ranked educational institutions in 2017. We collected the posts from each HEI official Facebook page during an entire school year. Our method showed high degree of accuracy and is also capable of describing which topics are most common in each university's social media content strategy and relate them to the corresponding response from their publics.

Abstract
A few years back organizations were rushing into social media environments following the worldwide trend to create a social presence in multiple channels and / or to explore their potential. Currently, after having gone through a period of experimentation and consolidation of that presence, it is important to understand and to report on how the performance and communication efficiency of organizations has evolved. On previous studies, where we focused on the public higher education sector, we have identified a set of organizations that presented behaviour which was typical from yearly social media adopters, with very low relative performance and communication efficiency. Using data and text mining tools, and techniques, we showed that these organizations revealed very low frequency of publication of messages and very low engagement among their audiences. At the time, the analysis of this sector posed challenges to the confirmation of whether these content strategies were representative enough and if they were a result of an effective and permanent organizational behaviour on social media, or just a result of a stage of social media adoption. In this paper, we present a longitudinal study that portrays the evolution of the organizational behaviour of these organizations on social media, concerning their relative performance and their communication efficiency after a four-year period. Our analysis is based on how and if they have evolved from that stage by fine-tuning their social media communications. We also present findings concerning the content strategy structure evolution along the past four years, concerning the type of content used in higher education institutions' social media strategies, to obtain the best possible return on engagement from the publics (fans), demonstrating how these organizations have either dropped Facebook or optimized their type of content to foster higher return. Thus, on this longitudinal study we present and benchmark the current state of performance of public higher education institutions, concerning the path they undertook in the past four years.

Abstract
The proliferation of false information on social networks is one of the hardest challenges in today's society, with implications capable of changing users perception on what is a fact or rumor. Due to its complexity, there has been an overwhelming number of contributions from the research community like the analysis of specific events where rumors are spread, analysis of the propagation of false content on the network, or machine learning algorithms to distinguish what is a fact and what is "fake news". In this paper, we identify and summarize some of the most prevalent works on the different categories studied. Finally, we also discuss the methods applied to deceive users and what are the next main challenges of this area.

Abstract
Nowadays, students commonly use and are assessed through an online platform. New pedagogy theories that promote the active participation of students in the learning process, and the systematic use of problem-based learning, are being adopted using an eLearning system for that purpose. However, although there can be intense feedback from these activities to students, usually it is restricted to the assessments of the online set of tasks. We propose a model that informs students of abnormal deviations of a “correct” learning path. Our approach is based on the vision that, by obtaining this information earlier in the semester, may provide students and educators an opportunity to resolve an eventual problem regarding the student’s current online actions towards the course. In the major learning management systems available, the interaction between the students and the system, is stored in log. Our proposal uses that logged information, and new one computed by our methodology, such as the time each student spends on an activity, the number and order of resources used, to build a table that a machine learning algorithm can learn from. Results show that our model can predict with more than 86% accuracy the failing situations. Copyright

Abstract
In this article we propose an automatic system that informs students of abnormal deviations of a virtual learning path that leads to the best grades in the course. Our motivation is based on the fact that by obtaining this information earlier in the semester, may provide students and educators an opportunity to resolve an eventual problem regarding the student's current online actions towards the course. Our goal is therefore to prevent situations that have a significant probability to lead to a pour grade and, eventually, to failing. Our methodology can be applied to online courses that integrate the use of an online platform that stores user actions in a log file, and that has access to other student's evaluations. The system is based on a data mining process on the log files and on a self-feedback machine learning algorithm that works paired with the Moodle LMS. Our results shown that it is possible to predict grade levels by only taking interaction patterns in consideration.

Abstract