The United States presidential elections receive a substantial attention not only from American voters, but also from news agencies, politicians, and international governments due to the local and global impact of the outcome. Therefore, different parties strive to predict the election’s results ahead of time, and opinion polls remain the predominant prediction method despite their bias and flaws. Online political communication has immensely evolved in recent years, especially on social media websites like Reddit, which has become a key platform in political discourse offering a valuable resource for studying public opinions on key issues. This study aims to utilize advanced machine learning methods to predict the outcome of the upcoming 2024 U.S. presidential election with a focus on the two primary candidates, former President Trump and Vice President Harris. Employing deep learning techniques to analyze more than 25 thousand online posts on Reddit, the results indicate that on the national level, Harris has more favorable sentiment in comparison to Trump among online users. However, analyzing the data associated with the battleground states, our model predicts that Trump has an edge over Harris, which may result in Trump winning the majority of the electoral votes in these states. This study underscores the importance of integrating social media data with machine learning capabilities for enhanced data-driven forecasts in upcoming elections and major public events.

Cyber-attacks are increasingly becoming a major concern for individuals and organizations alike. Meanwhile, attackers are employing advanced techniques taking advantage of the growing power of Artificial Intelligence (AI) to develop highly sophisticated attacks at an accelerated pace. Consequently, developing effective tools to detect cyber-attacks and protect digital assets has become of utmost importance to practice and research. Numerous AI-based techniques, mainly Machine Learning (ML) and Deep Learning (DL), have been investigated in the literature to protect digital assets from cyber threats. Particularly, DL has received significant attention in recent cybersecurity research due to its powerful capabilities in managing huge amounts of data and detecting malicious cyber threats. The majority of the proposed threat-detecting techniques in the literature focus on using Deep Neural Networks (DNNs). However, some research in the literature utilized the capabilities of Convolutional Neural Networks (CNNs) in detecting cyber threats by converting the cybersecurity data from tabular data into images. This paper aims to investigate the efficiency of both approaches in detecting cyber threats with the help of eXplainable AI (XAI). Our findings indicate that on NSL-KDD, the DL model trained on tabular features achieved WeightedF1 = 0.74, MacroF1 = 0.56, and Overall Accuracy (OA) = 0.78, compared with 0.73, 0.54, and 0.76 for the image-based model. On CICMaldoid20, the tabular model achieved 0.79 (WeightedF1), 0.80 (MacroF1), and 0.80 (OA), compared with 0.76, 0.75, and 0.77 for the image-based counterpart. These results suggest that while image transformations can be beneficial in specific classes, tabular models consistently deliver stronger overall performance. Received: 25 June 2025 | Revised: 12 November 2025 | Accepted: 21 November 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data are available from the corresponding author upon reasonable request. Author Contribution Statement Malik AL-Essa: Conceptualization, Methodology, Software, Validation, Formal analysis, Resources, Data curation, Writing – original draft, Writing – review & editing. Mohammad Alsharo: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – review & editing. Yazan Alnsour: Conceptualization, Methodology, Software, Validation, Resources, Data curation, Writing – review & editing. Wasim A. Ali: Conceptualization, Methodology, Software, Validation, Formal analysis, Resources, Data curation, Writing – review & editing. Omar Almomani: Writing – review & editing, Project administration.

Consumers are increasingly utilizing their smartphones to pay for goods and services, taking advantage of a variety of mobile payment options. Among these, Peer-to-Peer (P2P) mobile payment systems have gained global momentum, becoming one of consumers’ preferred choices. This study aims to examine the factors influencing consumers’ continuance intention to use CliQ, a P2P mobile payment system in Jordan. Following a thorough literature review, we extend the Theory of Planned Behavior (TPB) by integrating perceived structural assurance, perceived usefulness, and satisfaction. The Partial Least Squares Structural Equation Modeling (PLS-SEM) results indicate that perceived structural assurance significantly affects both consumer attitude and perceived security. The findings also suggest that attitude is the most influential factor in the proposed research model, while perceived usefulness and perceived behavioral control emerged as key drivers of user satisfaction and continuance intention. Furthermore, satisfaction was found to be a strong predictor of consumers’ continuance intention. These findings enrich the literature and provide valued implications for mobile-payment service providers and application developers.

An international association advancing the multidisciplinary study of informing systems. Founded in 1998, the Informing Science Institute (ISI) is a global community of academics shaping the future of informing science.

Aim/Purpose: This study aims to identify the characteristics of YouTube video reviews that consumers utilize to evaluate review helpfulness and explores how they process such information. This study aims to investigate the effect of argument quality, review popularity, number of likes, and source credibility on consumers’ perception of YouTube’s video review helpfulness. Background: Video reviews posted on YouTube are an emerging form of online reviews, which have the potential to be more helpful than textual reviews due to their visual and audible cues that deliver more vivid information about product features and specifications. With the availability of an enormous number of video reviews with unpredictable quality, it becomes challenging for consumers to find helpful reviews without consuming significant time and effort. In addition, YouTube does not provide a specific feature that indicates a review helpfulness similar to the one found on e-commerce websites. Consequently, consumers have to examine the characteristics of video reviews that are readily available on YouTube, evaluate them, and form a perception of whether a review is helpful or not. Despite the increasing popularity of YouTube’s video reviews, video reviews’ helpfulness received inadequate attention in the literature. The antecedents of the helpfulness of online video reviews are still underinvestigated, and more research is needed to identify the characteristics that consumers depend upon to assess video review helpfulness. Furthermore, it is important to understand how consumers process the information they gain from these characteristics to form a perception of their helpfulness. Methodology: Following an extended investigation of the relevant literature, we identified four key video characteristics that consumers presumably utilize to evaluate review helpfulness on YouTube (i.e., review popularity, number of likes, source credibility, and argument quality). By employing the Elaboration Likelihood Model (ELM), we classified these characteristics along the central and peripheral routes. The central route characteristics require a high cognitive effort by consumers to process the review’s message and reach a logical decision. In contrast, the peripheral route assumes that consumers judge the review’s message based on superficial qualities without substantial cognitive effort. A research model is introduced to investigate the effect of central and peripheral cues and their corresponding video review characteristics on review helpfulness. Accordingly, argument quality is proposed in the central route of the model, while review popularity, number of likes, and source credibility are proposed in the peripheral route. Furthermore, the study investigates how consumers process the information they obtain from these routes jointly or independently. To empirically test the proposed model, a convenient sample of 361 YouTube users was obtained through an online survey. The partial least squares method was used to investigate the effect of the proposed characteristics on video review helpfulness. Contribution: This study contributes to the literature in several ways. First, it is one of the few studies that investigate online video reviews’ helpfulness. Second, this study identifies several unique characteristics of YouTube’s video reviews that span peripheral and central routes, which potentially contribute to review helpfulness. Third, this study proposes a conceptual model based on the ELM to explore the effect of central and peripheral cues and their corresponding review characteristics on review helpfulness. Fourth, the research findings provide implications for research and practice that advance the theoretical understanding of video reviews’ helpfulness and serve as guidelines to create more helpful video reviews by better understanding the consumer’s cognitive processes. Findings: The results show that among the four characteristics proposed in the research model, argument quality in the central route is the strongest determinant factor affecting video review helpfulness. Results also show that review popularity, source credibility, and the number of likes in the peripheral route have significant effects on video review helpfulness. Altogether, our results show that the effect of the peripheral route adds up to 0.463 compared to 0.430, which is the impact magnitude of the argument quality construct in the central route. Based on the comparable effect magnitude of the central and peripheral routes of the model on video review helpfulness, our results indicate that both peripheral and central cues significantly affect consumers’ perception of video review helpfulness. The two routes are not mutually exclusive, and their cues can be processed in parallel or consecutive ways. Recommendations for Practitioners: The study recommends creating a dedicated category for reviews on YouTube with a specific feature for consumers to indicate the helpfulness of a video review, similar to the helpful vote button in textual reviews. The study also recommends that reviewers deliver more appealing and convincing argument quality, work toward improving their credibility, and understand the factors that contribute to video popularity. Impact on Society: Identifying the characteristics that affect video review helpfulness on YouTube helps consumers access helpful reviews more efficiently and improves their purchase decisions. Future Research: Future research could look into different types of data that could be extracted from YouTube to investigate the helpfulness of online video reviews. Future studies could employ machine learning and sentiment analysis techniques to reach more insights. Future research could also investigate the effect of product types in the context of online video reviews.