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In this paper, we address the need to study automatic propaganda detection to establish a course of action when faced with such a complex task. Although many isolated tasks have been proposed, a roadmap on how to best approach a new task from the perspective of text formality or the leverage of existing resources has not been explored yet.

We present a comprehensive study using several datasets on textual propaganda and different techniques to tackle it. We explore diverse collections with varied characteristics and analyze methodologies, from classic machine learning algorithms, to multi-task learning to utilize the available data in such models.

Our results show that transformer-based approaches are the best option with high-quality collections, and emotionally enriched inputs improve the results for Twitter content. Additionally, MTL achieves the best results in two of the five scenarios we analyzed. Notably, in one of the scenarios, the model achieves an F1 score of 0.78, significantly surpassing the transformer baseline model’s F1 score of 0.68.

After finding a positive impact when leveraging propaganda’s emotional content, we propose further research into exploiting other complex dimensions, such as moral issues or logical reasoning.

Based on our findings, we provide a roadmap for tackling propaganda-related tasks, depending on the types of training data available and the task to solve. This includes the application of MTL, which has yet to be fully exploited in propaganda detection.

In light of the burgeoning interest in the Metaverse within educational contexts, this study provides a comprehensive review to address the knowledge gap prevalent among K-12 and higher education teachers and educators. The increasing integration of the Metaverse into classroom settings necessitates a systematic exploration of its impact on subject-specific pedagogy, assessment methods, research methodologies and overall learning outcomes.

The research design involves a systematic review of 34 selected studies published between 2009 and 2023. The inclusion criteria prioritize investigations into Metaverse applications in classroom teaching, with a focus on subject disciplines, pedagogical approaches, measurement metrics and research methodologies. The selected studies undergo a detailed analysis and synthesis to extract meaningful patterns and trends.

The analysis reveals a predominant concentration on higher education, encompassing both science, technology, engineering, and mathematics (STEM) and non-STEM disciplines. However, few studies adopt comprehensive experimental designs. This suggests a gap in the methodological rigor of current research on Metaverse integration in education. While the Metaverse's impact on higher education is evident, the study underscores the need for more robust experimental designs and comprehensive research methodologies.

The limited exploration of students' experiences with the Metaverse in educational settings highlights a nascent research area that warrants further investigation. Future research should prioritize measuring students' perceptions and performance through indicators such as test grades to enhance our understanding of the Metaverse's efficacy in educational contexts.

The study aims to offer insights into the current state of Metaverse integration in education and identify areas for further research and development.