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This study aims to investigate how phishers apply persuasion principles and construct deceptive URLs in mobile instant messaging (MIM) phishing.

In total, 67 examples of real-world MIM phishing attacks were collected from various online sources. Each example was coded using established guidelines from the literature to identify the persuasion principles, and the URL construction techniques employed.

The principles of social proof, liking and authority were the most widely used in MIM phishing, followed by scarcity and reciprocity. Most phishing examples use three persuasion principles, often a combination of authority, liking and social proof. In contrast to email phishing but similar to vishing, the social proof principle was the most commonly used in MIM phishing. Phishers implement the social proof principle in different ways, most commonly by claiming that other users have already acted (e.g. crafting messages that indicate the sender has already benefited from the scam). In contrast to email, retail and fintech companies are the most commonly targeted in MIM phishing. Furthermore, phishers created deceptive URLs using multiple URL obfuscation techniques, often using spoofed domains, to make the URL complex by adding random characters and using homoglyphs.

The insights from this study provide a theoretical foundation for future research on the psychological aspects of phishing in MIM apps. The study provides recommendations that software developers should consider when developing automated anti-phishing solutions for MIM apps and proposes a set of MIM phishing awareness training tips.

The purpose of this paper is to perform a comparative analysis of the delay associated in running two real-time machine learning-based applications, namely, a video quality assessment (VQA) and a phishing detection application by using the edge, fog and cloud computing paradigms.

The VQA algorithm was developed using Android Studio and run on a mobile phone for the edge paradigm. For the fog paradigm, it was hosted on a Java server and for the cloud paradigm on the IBM and Firebase clouds. The phishing detection algorithm was embedded into a browser extension for the edge paradigm. For the fog paradigm, it was hosted on a Node.js server and for the cloud paradigm on Firebase.

For the VQA algorithm, the edge paradigm had the highest response time while the cloud paradigm had the lowest, as the algorithm was computationally intensive. For the phishing detection algorithm, the edge paradigm had the lowest response time, and the cloud paradigm had the highest, as the algorithm had a low computational complexity. Since the determining factor for the response time was the latency, the edge paradigm provided the smallest delay as all processing were local.

The main limitation of this work is that the experiments were performed on a small scale due to time and budget constraints.

A detailed analysis with real applications has been provided to show how the complexity of an application can determine the best computing paradigm on which it can be deployed.

Spam emails classification using data mining and machine learning approaches has enticed the researchers' attention duo to its obvious positive impact in protecting internet users. Several features can be used for creating data mining and machine learning based spam classification models. Yet, spammers know that the longer they will use the same set of features for tricking email users the more probably the anti-spam parties might develop tools for combating this kind of annoying email messages. Spammers, so, adapt by continuously reforming the group of features utilized for composing spam emails. For that reason, even though traditional classification methods possess sound classification results, they were ineffective for lifelong classification of spam emails duo to the fact that they might be prone to the so-called “Concept Drift”. In the current study, an enhanced model is proposed for ensuring lifelong spam classification model. For the evaluation purposes, the overall performance of the suggested model is contrasted against various other stream mining classification techniques. The results proved the success of the suggested model as a lifelong spam emails classification method.

Wikipedia's inclusive editorial policy permits unrestricted participation, enabling individuals to contribute and disseminate their expertise while drawing upon a multitude of external sources. News media outlets constitute nearly one-third of all citations within Wikipedia. However, embracing such a radically open approach also poses the challenge of the potential introduction of biased content or viewpoints into Wikipedia. The authors conduct an investigation into the integrity of knowledge within Wikipedia, focusing on the dimensions of source political polarization and trustworthiness. Specifically, the authors delve into the conceivable presence of political polarization within the news media citations on Wikipedia, identify the factors that may influence such polarization within the Wikipedia ecosystem and scrutinize the correlation between political polarization in news media sources and the factual reliability of Wikipedia's content.

The authors conduct a descriptive and regression analysis, relying on Wikipedia Citations, a large-scale open dataset of nearly 30 million citations from English Wikipedia. Additionally, this dataset has been augmented with information obtained from the Media Bias Monitor (MBM) and the Media Bias Fact Check (MBFC).

The authors find a moderate yet significant liberal bias in the choice of news media sources across Wikipedia. Furthermore, the authors show that this effect persists when accounting for the factual reliability of the news media.

The results contribute to Wikipedia’s knowledge integrity agenda in suggesting that a systematic effort would help to better map potential biases in Wikipedia and find means to strengthen its neutral point of view policy.

This paper constitutes the first academic study to be made of Google Discover as applied to online journalism.

This paper constitutes the first academic study to be made of Google Discover as applied to online journalism. The study involved conducting 61 semi-structured interviews with experts that are representative of a range of different professional profiles within the fields of journalism and search engine positioning (SEO) in Brazil, Spain and Greece. Based on the data collected, the authors created five semantic categories and compared the experts' perceptions in order to detect common response patterns.

This study results confirm the existence of different degrees of convergence and divergence in the opinions expressed in these three countries regarding the main dimensions of Google Discover, including specific strategies using the feed, its impact on web traffic, its impact on both quality and sensationalist content and on the degree of responsibility shown by the digital media in its use. The authors are also able to propose a set of best practices that journalists and digital media in-house web visibility teams should take into account to increase their probability of appearing in Google Discover. To this end, the authors consider strategies in the following areas of application: topics, different aspects of publication, elements of user experience, strategic analysis and diffusion and marketing.

Although research exists on the application of SEO to different areas, there have not, to date, been any studies examining Google Discover.

The peer-review history for this article is available at: https://publons.com/publon/10.1108/OIR-10-2022-0574

Social engineering is crucial in today’s digital landscape. As technology advances, malicious individuals exploit human judgment and trust. This study explores how age, education and occupation affect individuals’ awareness, skills and perceptions of social engineering.

A quantitative research approach was used to survey a diverse demographic of Egyptian society. The survey was conducted in February 2023, and the participants were sourced from various Egyptian social media pages covering different topics. The collected data was analyzed using descriptive and inferential statistics, including independent samples t-test and ANOVA, to compare awareness and skills across different groups.

The study revealed that younger individuals and those with higher education tend to research social engineering more frequently. Males display a higher level of awareness but score lower in terms of social and psychological consequences as well as types of attacks when compared to females. The type of attack cannot be predicted based on age. Higher education is linked to greater awareness and ability to defend against attacks. Different occupations have varying levels of awareness, skills, and psychosocial consequences. The study emphasizes the importance of increasing awareness, education and implementing cybersecurity measures.

This study’s originality lies in its focus on diverse Egyptian demographics, innovative recruitment via social media, comprehensive exploration of variables, statistical rigor, practical insights for cybersecurity education and diversity in educational and occupational backgrounds.

The rapid expansion of internet usage and device connectivity has underscored the importance of understanding the public’s cyber behavior and knowledge. Despite this, there is little research that examines the public’s objective knowledge of secure information security practices. The purpose of this study is to examine how objective cyber awareness is distributed throughout society.

This study draws on a large national survey of adults to examine the relationship between individual factors – such as demographic attributes and socioeconomic resources – and information security awareness. The study estimates several statistical models using weighted logistic regression to model objective information security awareness.

The results indicate that socioeconomic resources such as income and education have a significant effect on individuals’ information security awareness with richer and more highly educated individuals exhibiting greater awareness of important security practices and tools. Additionally, age and gender represent consistent and clear informational gaps in society as older individuals and females are significantly less knowledgeable about an array of information security practices than younger individuals and males, respectively.

The findings have important implications for our understanding of information security behavior and user vulnerability in an increasingly digital and connected society. Despite the growing importance of cybersecurity for all individuals in nearly all domains of daily life, there is substantial inequality in awareness about secure cyber practices and the tools and techniques used to protect one’s self from attacks. While digital technology will continue to permeate many aspects of daily life – from financial transactions to health services to social interactions – the findings here indicate that some users may be far more exposed and vulnerable to attack than others.

This study contributes to our understanding of general user information security awareness using a large survey and statistical models to generalize about the public’s information security awareness across multiple domains and stimulates future research on public knowledge of information security. The findings indicate that some users may be far more exposed and vulnerable to attack than others. Despite the growing importance of cybersecurity for all individuals in nearly all domains of daily life, there is substantial inequality in awareness about secure cyber practices and the tools and techniques used to protect one’s self from attacks.

A clickbait is a deceptive headline designed to boost ad revenue without presenting closely relevant content. There are numerous negative repercussions of clickbait, such as causing viewers to feel tricked and unhappy, causing long-term confusion, and even attracting cyber criminals. Automatic detection algorithms for clickbait have been developed to address this issue. The fact that there is only one semantic representation for the same term and a limited dataset in Chinese is a need for the existing technologies for detecting clickbait. This study aims to solve the limitations of automated clickbait detection in the Chinese dataset.

This study combines both to train the model to capture the probable relationship between clickbait news headlines and news content. In addition, part-of-speech elements are used to generate the most appropriate semantic representation for clickbait detection, improving clickbait detection performance.

This research successfully compiled a dataset containing up to 20,896 Chinese clickbait news articles. This collection contains news headlines, articles, categories and supplementary metadata. The suggested context-aware clickbait detection (CA-CD) model outperforms existing clickbait detection approaches on many criteria, demonstrating the proposed strategy's efficacy.

The originality of this study resides in the newly compiled Chinese clickbait dataset and contextual semantic representation-based clickbait detection approach employing transfer learning. This method can modify the semantic representation of each word based on context and assist the model in more precisely interpreting the original meaning of news articles.