The ubiquitous access and exponential growth of information available on social media networks have facilitated the spread of fake news, complicating the task of distinguishing between this and real news. Fake news is a significant social barrier that has a profoundly negative impact on society. Despite the large number of studies on fake news detection, they have not yet been combined to offer coherent insight on trends and advancements in this domain. Hence, the primary objective of this study was to fill this knowledge gap. The method for selecting the pertinent articles for extraction was created using the preferred reporting items for systematic reviews and meta-analyses (PRISMA). This study reviewed deep learning, machine learning, and ensemble-based fake news detection methods by a meta-analysis of 125 studies to aggregate their results quantitatively. The meta-analysis primarily focused on statistics and the quantitative analysis of data from numerous separate primary investigations to identify overall trends. The results of the meta-analysis were reported by the spatial distribution, the approaches adopted, the sample size, and the performance of methods in terms of accuracy. According to the statistics of between-study variance high heterogeneity was found with τ² = 3.441; the ratio of true heterogeneity to total observed variation was I² = 75.27% with the heterogeneity chi-square (Q) = 501.34, the degree of freedom = 124, and p ≤ 0.001. A p-value of 0.912 from the Egger statistical test confirmed the absence of a publication bias. The findings of the meta-analysis demonstrated satisfaction with the effectiveness of the recommended approaches from the primary studies on fake news detection that were included. Furthermore, the findings can inform researchers about various approaches they can use to detect online fake news.

The COVID-19 pandemic created the need for a global change in tertiary education. Universities that traditionally relied on contact with students in physical classrooms were forced to consider modes of remote teaching to mitigate the risks of infection due to physical proximity. This study evaluates the emergency remote teaching implemented within the Department of Information Technology at the Durban University of Technology, South Africa. An emergency remote teaching model with four stages consisting of: preparation, synchronous and asynchronous teaching and learning, e-assessments and reflections are described, analysed and evaluated with reference to both lecturers and students. The evaluation is performed using both qualitative and quantitative research methods. Qualitative analysis was performed on 29 sources using content analysis. 229 initial codes were identified and first categorized into 13 subcategories and finally to the four categories synonymous with the adopted four-stage emergency remote teaching model: preparation (135 references), asynchronous and synchronous teaching and learning (67 references), e-assessments (25 references) and reflections (8 references). Quantitative data on the use of the learning management system from 2019 to 2020 evaluated the results of the applied changes in practice. From the results, it was evident that students and lecturers invested much time in the learning management system with 13 tools being adopted by the 49 analysed subjects. The learning management system was used extensively for communication, assessment and dissemination of subject content. The comparative results of the data from the 2019 and 2020 academic years showed that the majority of the 2020 subjects’ final results were statistically higher than the 2019 results. Results of analysis revealed the success of the implementation of the four-stage emergency remote teaching model. Received: 29 June 2021Accepted: 26 April 2022

A novel method to cross impact matrix creation is presented in this paper to overcome the inherent challenges of cross impact analysis. The cross impact matrix is a strong prerequisite for numerous impact factor analysis methods which are used in future studies, military intelligence, game simulation and various business contexts. The method draws on the property of transitivity of preference as a key concept for the creation of the cross impact matrix from preference chains. A number of drawbacks associated with the traditional development and creation of the cross impact matrix are addressed and are overcome using this novel approach. An existing cross impact matrix is used to test the validity and reliability of the method developed.

Cross-impact analysis is frequently used in scenario-analogous studies to identify critical factors influencing ecological change, strategic planning, technology foresight, resource allocation, risk mitigation, cost optimization, and decision support. Scenarios enable different organizations to comprehend prevailing situations, prepare for probable futures, and mitigate conceivable risks. Unfortunately, cross-impact analysis methods are often criticized for their difficulty in handling complex interactions, cognitive bias, time-intensiveness, heavy reliance on a limited pool of experts, and inconsistency in assigning judgment, which can affect the expected outcomes. This paper introduces a novel method for constructing consistent scenarios that addresses these criticisms and those associated with scenario methods. The method is based on cross-impact analysis and crowdsourcing for constructing consistent scenarios. The cross-impact analysis component of the method is based on advanced impact analysis and cross-impact balance analysis to, respectively, provide a time-efficient reduction in complex interdependent factors and construct consistent scenarios from a set of reduced factors. The crowdsourcing element leverages the cumulative intelligence of a group of experts to help mitigate cognitive bias and transparently give a more inclusive analysis. The method was implemented and validated with a practical case of renewable energy adoption, a vital challenge for socioeconomic progress and climate change resilience. While the method provides a sturdy foundation for writing scenario narratives, the result confirms its robustness for constructing consistent scenarios and suggests that the future of renewable energy adoption can be enhanced through careful cogitation of best-case, base-case, and worst-case scenarios, which include varying states of perceived value, awareness, and perceived support. These findings contribute to a more nuanced understanding of how socio-cognitive and institutional factors interact to influence the pace and direction of sustainable energy transitions.