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
There is nowadays a constant flux of data being generated and collected in all types of real world systems. These data sets are often indexed by time, space, or both requiring appropriate approaches to analyze the data. In univariate settings, time series analysis is a mature field. However, in multivariate contexts, time series analysis still presents many limitations. In order to address these issues, the last decade has brought approaches based on network science. These methods involve transforming an initial time series data set into one or more networks, which can be analyzed in depth to provide insight into the original time series. This review provides a comprehensive overview of existing mapping methods for transforming time series into networks for a wide audience of researchers and practitioners in machine learning, data mining, and time series. Our main contribution is a structured review of existing methodologies, identifying their main characteristics, and their differences. We describe the main conceptual approaches, provide authoritative references and give insight into their advantages and limitations in a unified way and language. We first describe the case of univariate time series, which can be mapped to single layer networks, and we divide the current mappings based on the underlying concept: visibility, transition, and proximity. We then proceed with multivariate time series discussing both single layer and multiple layer approaches. Although still very recent, this research area has much potential and with this survey we intend to pave the way for future research on the topic. This article is categorized under: Fundamental Concepts of Data and Knowledge > Data Concepts Fundamental Concepts of Data and Knowledge > Knowledge Representation