Social bots are automated social media accounts governed by software and controlled by humans at the backend. Some bots have good purposes, such as automatically posting information about news and even to provide help during emergencies. Nevertheless, bots have also been used for malicious purposes, such as for posting fake news or rumour spreading or manipulating political campaigns. There are existing mechanisms that allow for detection and removal of malicious bots automatically. However, the bot landscape changes as the bot creators use more sophisticated methods to avoid being detected. Therefore, new mechanisms for discerning between legitimate and bot accounts are much needed. Over the past few years, a few review studies contributed to the social media bot detection research by presenting a comprehensive survey on various detection methods including cutting-edge solutions like machine learning (ML)/deep learning (DL) techniques. This paper, to the best of our knowledge, is the first one to only highlight the DL techniques and compare the motivation/effectiveness of these techniques among themselves and over other methods, especially the traditional ML ones. We present here a refined taxonomy of the features used in DL studies and details about the associated pre-processing strategies required to make suitable training data for a DL model. We summarize the gaps addressed by the review papers that mentioned about DL/ML studies to provide future directions in this field. Overall, DL techniques turn out to be computation and time efficient techniques for social bot detection with better or compatible performance as traditional ML techniques.

The potential of artificial intelligence (AI)-based large language models (LLMs) holds considerable promise in revolutionising education, research and practice. However, distinguishing between human-written and AI-generated text has become a significant task. This article presents a comparative study, introducing a novel dataset of human-written and LLM-generated texts in different genres: essays, stories, poetry and Python code. We employ several machine learning models to classify the texts. Results demonstrate the efficacy of these models in discerning between human and AI-generated text, despite the dataset’s limited sample size. However, the task becomes more challenging when classifying GPT-generated text, particularly in story writing. The results indicate that the models exhibit superior performance in binary classification tasks, such as distinguishing human-generated text from a specific LLM, compared with the more complex multiclass tasks that involve discerning among human-generated and multiple LLMs. Our findings provide insightful implications for AI text detection, while our dataset paves the way for future research in this evolving area.

Purpose: Smart city services (SCS) in contrast with other technology-based services, demand significant interaction and collaboration between the users and the service providers. This study examines the SCS delivery-channel characteristics and the users' personal (behavioral and demographic) characteristics that influence their satisfaction or dissatisfaction with the services, as well as their intention to adopt (i.e. continue using) the SCS-delivery channels.Design/methodology/approach: A quantitative study using a structured questionnaire was conducted for this paper. The data-collection method was administered by emailing the survey to a list of 2,350 city/urban residents who are members of the two largest universities in the greater Dubai metropolis. A total of 600 completed responses (26 percent) were received back, while 580 useable responses (25 percent) were analyzed for this paper.Findings: Our initial findings suggest that contrary to popular belief, it is not only SCS channel factors that influence user satisfaction and continuance intention. SCS users' personal characteristics (such as their user innovativeness and control-seeking behavior) are also pivotal in determining their satisfaction and intention to continue or not continue using the SCS-delivery channels.Research limitations/implications: The paper argues that both SCS channel factors and SCS users' personal characteristics jointly influence the users' experience of the services and therefore jointly determine their satisfaction with the service as well as their SCS usage continuance intention. The result of our research gives important insights into users' behaviors toward the emerging SCS channels in general, and it will be of great value to architects and designers of Smart City technologies around the world.Practical implications: The paper argues that both SCS channel factors and SCS users' personal (behavioral and demographic) characteristics jointly influence the users' trials of the services, and therefore jointly determine their satisfaction with the service as well as their SCS usage continuance intention. The result of our research gives important insights into users' behavioral intentions toward the emerging SCS channels in general; and it will be of great value to architects and designers of Smart City technologies around the world.Originality/value: This paper is one of the first few studies focused on investigating the antecedents of SCS usage behaviors in the Middle Eastern region.