Chapter 11: Astrobiology Education, Engagement, and Resources

Although astrobiology is a relatively new field of science, the questions it seeks to answer (e.g., "What is life?" "What does life require?") have been investigated for millennia. In recent decades, formal programs dedicated specifically to the science of astrobiology have been organized at academic, governmental, and institutional scales. Constructing educational programs around this emerging science relies on input from broad expertise and backgrounds. Because of the interdisciplinary nature of this field, career pathways in astrobiology often begin in more specific fields such as astronomy, geology, or biology, and unlike many other sciences, typically involve substantial training outside one's primary discipline. The recent origin of astrobiology as a field of science has led to strong collaborations with education research in the development of astrobiology courses and offers a unique instructional laboratory for further pedagogical studies. This chapter is intended to support students, educators, and early career scientists by connecting them to materials and opportunities that the authors and colleagues have found advantageous. Annotated lists of relevant programs and resources are included as a series of appendices in the supplementary material.

Fake news, disinformation and misinformation in social media: a review

Online social networks (OSNs) are rapidly growing and have become a huge source of all kinds of global and local news for millions of users. However, OSNs are a double-edged sword. Although the great advantages they offer such as unlimited easy communication and instant news and information, they can also have many disadvantages and issues. One of their major challenging issues is the spread of fake news. Fake news identification is still a complex unresolved issue. Furthermore, fake news detection on OSNs presents unique characteristics and challenges that make finding a solution anything but trivial. On the other hand, artificial intelligence (AI) approaches are still incapable of overcoming this challenging problem. To make matters worse, AI techniques such as machine learning and deep learning are leveraged to deceive people by creating and disseminating fake content. Consequently, automatic fake news detection remains a huge challenge, primarily because the content is designed in a way to closely resemble the truth, and it is often hard to determine its veracity by AI alone without additional information from third parties. This work aims to provide a comprehensive and systematic review of fake news research as well as a fundamental review of existing approaches used to detect and prevent fake news from spreading via OSNs. We present the research problem and the existing challenges, discuss the state of the art in existing approaches for fake news detection, and point out the future research directions in tackling the challenges.

Automatic Fake News Detection for Romanian Online News

This paper proposes a supervised machine learning system to detect fake news in online sources published in Romanian. Additionally, this work presents a comparison of the obtained results by using recurrent neural networks based on long short-term memory and gated recurrent unit cells, a convolutional neural network, and a Bidirectional Encoder Representations from Transformers (BERT) model, namely RoBERT, a pre-trained Romanian BERT model. The deep learning architectures are compared with the results achieved by two classical classification algorithms: Naïve Bayes and Support Vector Machine. The proposed approach is based on a Romanian news corpus containing 25,841 true news items and 13,064 fake news items. The best result is over 98.20%, achieved by the convolutional neural network, which outperforms the standard classification methods and the BERT models. Moreover, based on irony detection and sentiment analysis systems, additional details are revealed about the irony phenomenon and sentiment analysis field which are used to tackle fake news challenges.

Digitainability—Digital Competences Post-COVID-19 for a Sustainable Society

The digitalization of societies, accelerated by the COVID-19 pandemic, is an unstoppable process. This paper seeks to answer the question: what post-COVID-19 digital competences are needed for a sustainable society? It also aims to analyze the digitalization processes in education for shaping a sustainable digital society. A bibliographic search was performed on some of the most relevant international databases of scientific literature and the selected documents were analyzed through a content analysis. It is concluded that digital education has experienced a strong increase, reinforced by COVID-19, shaping the digital presence in all dimensions of life. However, it is not sufficient to assume that the new generations are naturally engaged in and can master digital social sustainability. The results demonstrate the importance of literacy and the unavoidable promotion of sustainability in a digital society. However, this digitalization of the educational process poses several challenges: it requires both software and hardware conditions, as well as digital literacy as a result of a complex of literacies. It also implies that teachers and students change their standpoints and practices with the attainment of new teaching and learning competences in order to fight the digital divide and to foster the widest possible social inclusion for the promotion of sustainable society—digitainability.

Memetics of Deception: Spreading Local Meme Hoaxes during COVID-19 1st Year

The central thesis of this paper is that memetic practices can be crucial to understanding deception at present when hoaxes have increased globally due to COVID-19. Therefore, we employ existing memetic theory to describe the qualities and characteristics of meme hoaxes in terms of the way they are replicated by altering some aspects of the original, and then shared on social media platforms in order to connect global and local issues. Criteria for selecting the sample were hoaxes retrieved from and related to the local territory in the province of Alicante (Spain) during the first year of the pandemic (n = 35). Once typology, hoax topics and their memetic qualities were identified, we analysed their characteristics according to form in terms of Shifman (2014) and, secondly, their content and stance concordances both within and outside our sample (Spain and abroad). The results show, firstly, that hoaxes are mainly disinformation and they are related to the pandemic. Secondly, despite the notion that local hoaxes are linked to local circumstances that are difficult to extrapolate, our conclusions demonstrate their extraordinary memetic and “glocal” capacity: they rapidly adapt other hoaxes from other places to local areas, very often supplanting reliable sources, and thereby demonstrating consistency and opportunism.

Sentiment Analysis for Fake News Detection

In recent years, we have witnessed a rise in fake news, i.e., provably false pieces of information created with the intention of deception. The dissemination of this type of news poses a serious threat to cohesion and social well-being, since it fosters political polarization and the distrust of people with respect to their leaders. The huge amount of news that is disseminated through social media makes manual verification unfeasible, which has promoted the design and implementation of automatic systems for fake news detection. The creators of fake news use various stylistic tricks to promote the success of their creations, with one of them being to excite the sentiments of the recipients. This has led to sentiment analysis, the part of text analytics in charge of determining the polarity and strength of sentiments expressed in a text, to be used in fake news detection approaches, either as a basis of the system or as a complementary element. In this article, we study the different uses of sentiment analysis in the detection of fake news, with a discussion of the most relevant elements and shortcomings, and the requirements that should be met in the near future, such as multilingualism, explainability, mitigation of biases, or treatment of multimedia elements.

The Contribution of the Flipped Classroom Method to the Development of Information Literacy: A Systematic Review

With the arrival of the Internet and information and communication technologies (ICTs), today’s society has driven many changes in which the digital channel is one of the main information consumption channels for young university students. Due to the turbulent landscape full of fake news and confusing information we have entered, there is a need to promote information literacy among quality students. To this end, the use of innovative strategies and methods such as Flipped Classroom can be a novel alternative to receive this content. Based on these ideas, the objective of this work was framed in the methodological design corresponding to the systematic review. Thus, empirical work was compiled to put into practice the Flipped Classroom methodology and to promote informational teaching from different knowledge disciplines at the university level. The results showed that the application of the methodology is multivariate, and that it can be administered to students in various ways (video-tutorials, animated videos, training modules, etc.) depending on the creativity of the teacher. Likewise, the results compiled in the review turn towards the improvement of the students’ informational skills, as well as the increase in diverse personal and academic variables.