Abstract
In recent years, the problem of unreliable content in social networks has become a major threat, with a proven real-world impact in events like elections and pandemics, undermining democracy and trust in science, respectively. Research in this domain has focused not only on the content but also on the accounts that propagate it, with the bot detection task having been thoroughly studied. However, not all bot accounts work as unreliable content spreaders (p.e. bot for news aggregation), and not all human accounts are necessarily reliable. In this study, we try to distinguish unreliable from reliable accounts, independently of how they are operated. In addition, we work towards providing a methodology capable of coping with real-world situations by introducing the content available (restricting it by volume- and time-based batches) as a parameter of the methodology. Experiments conducted on a validation set with a different number of tweets per account provide evidence that our proposed solution produces an increase of up to 20% in performance when compared with traditional (individual) models and with cross-batch models (which perform better with different batches of tweets).

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
IntroductionPeople with schizophrenia experience higher levels of stigma compared with other diseases. The analysis of social media content is a tool of great importance to understand the public opinion toward a particular topic.ObjectivesThe aim of this study is to analyse the content of social media on schizophrenia and the most prevalent sentiments towards this disorder.MethodsTweets were retrieved using Twitter’s Application Programming Interface and the keyword “schizophrenia”. Parameters were set to allow the retrieval of recent and popular tweets on the topic and no restrictions were made in terms of geolocation. Analysis of 8 basic emotions (anger, anticipation, disgust, fear, joy, sadness, surprise, and trust) was conducted automatically using a lexicon-based approach and the NRC Word-Emotion Association Lexicon.ResultsTweets on schizophrenia were heterogeneous. The most prevalent sentiments on the topic were mainly negative, namely anger, fear, sadness and disgust. Qualitative analyses of the most retweeted posts added insight into the nature of the public dialogue on schizophrenia.ConclusionsAnalyses of social media content can add value to the research on stigma toward psychiatric disorders. This tool is of growing importance in many fields and further research in mental health can help the development of public health strategies in order to decrease the stigma towards psychiatric disorders.

Abstract

Abstract
This research provides an overview of the online consumer experience of luxury brands in Portugal. The purpose of this study was to identify the significant factors that represent customers’ perceptions of the online shopping experience for luxury products. Using a quantitative approach, the authors conducted an online survey. 327 usable responses were obtained. Descriptive and factorial statistical analyzes were used to provide the empirical findings. This study proposes and empirically tests a model of the factorial structure of the online shopping experience for luxury goods. We found an eight-factor dimension structure that proposes the main contributors to understand the factors that represent consumer perceptions about buying luxury products online. The findings suggest that the eight ranked significant factors that represent the customer’s perception of the online luxury shopping experience are in this order: e-buying experience, e-loyalty, e-risk, e-satisfaction, luxury value, luxury useless, luxury future buy, and e-buying influence. The work provides empirical evidence that the eight significant factors represent the customer’s perception of the luxury shopping experience online, that help to understand how luxury brands should be managed online in order to enhance customer e-buying experience, e-satisfaction, e-loyalty, and luxury value proposition. This study provides several contributions for online luxury brand managers and some directions for further research. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Abstract
The COVID-19 pandemic situation has pushed many higher education institutions into a fast-paced, and mostly unstructured, emergency remote education process. In such an unprecedented context, it is important to understand how technology is mediating the educational process and how teachers and students are experiencing the change brought by the pandemic. This research aims to understand how the learning was mediated by technology during the early stages of the pandemic and how students and teachers experienced this sudden change. Data were collected following a qualitative research design. Thirty in-depth and semi-structured interviews (20 students and 10 teachers) were obtained and analysed following a thematic analysis approach. Results provide evidence on the adoption of remote education technologies due to the pandemic with impacts on the education process, ICT platforms usage and personal adaptation. The emergency remote education context led to mixed outcomes regarding the education process. Simultaneously, ICT platforms usage was mostly a positive experience and personal adaptation was mostly a negative experience. These results bring new insights for higher education organizations on actions they could take, such as curating the learning experience with standard, institutional-wide platforms, appropriate training for students and teachers, and suitable remote evaluation practices.