Social media networks such as Twitter, Facebook, YouTube, blogs, and discussion forums are becoming powerful tools that extremist groups use to disseminate radical ideologies and propaganda, and to recruit people to their cause. Identifying extremist social media content and profiles is a top priority for counter-terrorist agencies, technology companies, and governments. The main objective of this paper is to provide a better understanding of the definition of extremism, and a detailed review of the current research regarding online extremism in text. To identify gaps in the literature, a systematic literature review (SLR) of 45 studies published between 2015 and 2020 was undertaken, which revealed challenges, technical pitfalls in previous studies, and opportunities for extending and improving prior results in meaningful ways. The systematic review indicates the need for better understanding of the landscape and directions of the online extremism. This study offers a critical analysis of the new area of research.

The dissemination of extremist ideas and causes online has intensified over the last decade. Extremist organizations use social media to gain publicity and new recruits, often with little interference from network providers. New techniques are being developed to identify extremist content, ensuring it can be promptly removed and its authors blocked from network access. However, most techniques are only compatible with the English language, despite the fact that extremist propaganda is frequently shared in other languages, including Arabic. Since the most effective methods for automated linguistic analysis use deep learning and require large, high-quality datasets, creating specialised data samples containing examples of extremist communication is an essential step toward a practical solution. In this paper, we present a dataset compiled for this purpose and discuss the classification methods that can be used for extremism detection. The manually annotated Arabic Twitter dataset consists of 89,816 tweets published between 2011 and 2021. Using guidelines, three expert annotators labelled the tweets as extremist or non-extremist. Exploratory data analysis was performed to understand the dataset’s features. Classification algorithms were used with the dataset, including logistic regression, support vector machine, multinominal naïve Bayes, random forest, and BERT. Among the traditional machine learning models, support vector machine with term frequency-inverse document frequency features achieved the highest accuracy (0.9729). However, BERT outperformed the traditional models with an accuracy of 0.9749. This dataset is expected to enhance the accuracy of Arabic online extremism classification in future research, and so we have made it publicly available.

The rapid growth of electronic commerce, driven by technological advancements, has led to the emergence of social commerce (s-commerce), where social media platforms facilitate online transactions. Despite these advancements, consumer trust remains a critical factor for the success of online businesses due to the inherent uncertainties and perceived risks of virtual transactions. This study investigates the impact of consumer trust on social e-commerce among Saudi online consumers. By examining trust as a mediating factor, the study explores various dimensions such as communication, word-of-mouth marketing, economic viability, transaction safety, reputation, and information quality. Quantitative data were collected through a survey distributed across social networking sites, targeting frequent s-commerce users in Saudi Arabia. Structural equation modeling using partial least squares was employed to analyze the data. The findings reveal that reputation, transaction safety, and economic viability significantly enhance consumer trust, whereas information quality and communication show negligible effects. Additionally, consumer trust positively influences word-of-mouth intentions. This research contributes to the literature on online consumer behavior and provides insights for social commerce businesses in Saudi Arabia to develop strategies that enhance consumer trust.

This paper applies the k-nearest neighbor (k-NN) algorithm, recognized for its predictive accuracy, to investigate the relationship between global poverty rates and global carbon emissions. The analysis explores how variations in poverty levels influence environmental carbon emissions. The results demonstrate that the relationship between poverty rates and carbon emissions resembles the production possibility curve, showing an inverse relationship: as poverty rates increase, carbon emissions decrease. Specifically, the k-NN model reveals that a 1% rise in poverty rates leads to a 0.29% reduction in carbon emissions. This outcome reflects the tendency for emissions to fall when economic growth slows. The study concluded that the global quest to eradicate poverty is closely linked to environmental challenges. While reducing poverty contributes to improved living standards, it also threatens to increase emissions, necessitating a balance between economic growth and environmental sustainability. The findings highlight the urgent need for policies that reconcile poverty reduction and environmental conservation. A key recommendation is to rely heavily on renewable energy to support economic growth while reducing carbon emissions. This approach enables progress toward poverty reduction and environmental protection.