Abstract
Higher Education Institutions use social media as a marketing channel to attract and engage users so that the institution is promoted and thus a wide range of benefits can be achieved. These institutions are evaluated globally on various success parameters, being published in rankings. In this paper, we analyze the publishing strategies and compare the results with their overall ranking positions. The results show that there is a tendency to find a particular strategy in the top ranked universities. We also found cases where the strategies are less prominent and do not match the ranking positions. Copyright © 2022 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved.

Abstract

Abstract
In this paper we examine the use of social media as a marketing channel by Higher Education Institutions (HEI) and its impact on the institution's brand, attracting top professionals and students. HEIs are annually evaluated globally based on various success parameters to be published in rankings. Specifically, we analyze the Twitter publishing strategies of the selected HEIs, and we compare the results with their overall ranking positions. Our study shows that there are no significant differences between the well-known university rankings based on Kendall t and RBO metrics. However, our data retrieval indicates a tendency for the top-ranked universities to adopt specific strategies, which are further emphasized when analyzing emotions and topics. Conversely, some universities have less prominent strategies that do not align with their ranking positions. This study provides insights into the role of social media in the marketing strategies of HEIs and its impact on their global rankings. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract

Abstract

Abstract
Being able to capture the characteristics of a time series with a feature vector is a very important task with a multitude of applications, such as classification, clustering or forecasting. Usually, the features are obtained from linear and nonlinear time series measures, that may present several data related drawbacks. In this work we introduce NetF as an alternative set of features, incorporating several representative topological measures of different complex networks mappings of the time series. Our approach does not require data preprocessing and is applicable regardless of any data characteristics. Exploring our novel feature vector, we are able to connect mapped network features to properties inherent in diversified time series models, showing that NetF can be useful to characterize time data. Furthermore, we also demonstrate the applicability of our methodology in clustering synthetic and benchmark time series sets, comparing its performance with more conventional features, showcasing how NetF can achieve high-accuracy clusters. Our results are very promising, with network features from different mapping methods capturing different properties of the time series, adding a different and rich feature set to the literature.