Abstract
In recent years we have seen a large adherence to social media by various Higher Education Institutions (HEI) with the intent of reaching their target audiences and improve their public image. These institutional publications are guided by a specific editorial strategy, designed to help them better accomplish and fulfill their mission. The current Covid-19 pandemic has had major consequences in many different fields (political, economic, social, educational) beyond the spread of the disease itself. In this paper, we attempt to determine the impact of the pandemic on the HEI content strategies by gauging if these social-economical, cultural and psychological changes that occurred during this global catastrophe are actively reflected in their publications. Furthermore, we identified the topics that emerge from the pandemic situation checking the trend changes and the concept drift that many topics had. We gathered and analyzed more than 18k Twitter publications from 12 of the top HEI according to the 2019 Center for World University Rankings (CWUR). Utilizing machine learning techniques, and topic modeling, we determined the emergent content topics for each institution before, and during, the Covid-19 pandemic to uncover any significant differences in the strategies.

Abstract
The recent proliferation of so called fake news content, assisted by the widespread use of social media platforms and with serious real-world impacts, makes it imperative to find ways to mitigate this problem. In this paper we propose a machine learning-based approach to tackle it by automatically identifying tweets associated with questionable content, using newly-collected data from Twitter about the 2020 U.S. presidential election. To create a sizable annotated data set, we use an automatic labeling process based on the factual reporting level of links contained in tweets, as classified by human experts. We derive relevant features from that data and investigate the specific contribution of features derived from named entity and emotion recognition techniques, including a novel approach using sequences of prevalent emotions. We conclude the paper by evaluating and comparing the performance of several machine learning models on different test sets, and show they are applicable to addressing the issue of fake news dissemination.

Abstract
In recent years we have seen a large adherence to social media by various Higher Education Institutions (HEI) with the intent of reaching their target audiences and strengthen their brand recognition. It is important for organizations to discover the true audience-aggregating themes resulting from their communication strategies, as it provides institutions with the ability to monitor their organizational positioning and identify opportunities and threats. In this work we create an automatic system capable of identifying HEI Twitter communication strategies. We gathered and analyzed more than 18k Twitter publications from 12 of the top-HEI according to the 2019 Center for World University Rankings (CWUR). Results show that there are different strategies, and most of HEI had to adapt them to the covid situation. The analysis also shows the prediction of topics and retweets for a HEI cannot just be based on recent historical data.

Abstract
The negative impact of false information on social networks is rapidly growing. Current research on the topic focused on the detection of fake news in a particular context or event (such as elections) or using data from a short period of time. Therefore, an evaluation of the current proposals in a long-term scenario where the topics discussed may change is lacking. In this work, we deviate from current approaches to the problem and instead focus on a longitudinal evaluation using social network publications spanning an 18-month period. We evaluate different combinations of features and supervised models in a long-term scenario where the training and testing data are ordered chronologically, and thus the robustness and stability of the models can be evaluated through time. We experimented with 3 different scenarios where the models are trained with 15-, 30-, and 60-day data periods. The results show that detection models trained with word-embedding features are the ones that perform better and are less likely to be affected by the change of topics (for example, the rise of COVID-19 conspiracy theories). Furthermore, the additional days of training data also increase the performance of the best feature/model combinations, although not very significantly (around 2%). The results presented in this paper build the foundations towards a more pragmatic approach to the evaluation of fake news detection models in social networks.

Abstract

Abstract
As e-Learning systems have become gradually prevalent, forcing a (sometimes needed) physical distance between lecturers and their students, new methods need to emerge to fill this enlarging gap. Educators need, more than ever, systems capable of warning them (and the students) of situations that might create future problems for the learning process. The capacity to give and get feedback is naturally the best way to overcome this problem. However, in e-learning contexts, with dozens or hundreds of students, the solution becomes less simple. In this work we propose a system capable of continuously giving feedback on the performance of the students based on the interaction sequences they undertake with the LMS. This work innovates in what concerns the sequences of activity accesses together with the computation of the duration of these online learning activities, which are then encoded and fed into machine learning algorithms. We used a longitudinal experiment from five academic years. From our set of classifiers, the Random Forest obtained the best results for preventing low grades, with an accuracy of nearly 87%.