Search Results (14)

In recent years, there has been a sharp increase in the number of online fraud cases. However, research on crime geography has paid little attention to online crimes, especially to the influencing factors behind their spatial distributions. Online fraud is closely related to people’s daily internet use. The existing literature has explored the impact of internet use on online crimes based on small samples of individual interviews. There is a lack of large-scale studies from a community perspective. This study applies the routine activity theory to online activities to test the relationship between online fraud alert data and the usage durations of different types of mobile phone users’ applications (apps) for communities in ZG City. It builds negative binomial regression models for analyzing the impact of the usage of different types of apps on the spatial distribution of online fraud. The results reveal that the online fraud crime rate and the online time spent on a financial management app share the most similar spatial distribution. While financial management, online education, transportation, and search engine app usages have a significant positive association with online fraud, the use of a financial management app has the greatest impact. Additionally, time spent on social media, online shopping and entertainment, and mobile reading apps have a significant negative association with online fraud. As not all online activities lead to cybercrime, crime prevention efforts should target specific types of apps, such as financial management, online education, transportation, and search engines. Full article

Practical challenges and special threats from scam-related fraud exist for regional and local communities in Southeast Asia during and after the COVID-19 pandemic. The rise in pig-butchering operations in Southeast Asia is a major concern due to the increased use of digital technology and online financial transactions. Many of these operations are linked to organized crime syndicates operating across borders, posing challenges for law enforcement. As a first study in Vietnam, we combined the primary and secondary databases to unveil the nature of transnational scam-related fraud. Findings show that scammers are using advanced methods such as phishing, fraudulent investments, and identity theft to maximize their sophisticated tactics for achieving financial possession. There are organized crime rings operating in Vietnam and Cambodia, with Chinese groups playing a leading role behind the scenes. Social media and its various applications have become common platforms for these criminal activities. This study also calls for practical recommendations to consider specific challenges in combating these crimes, including building a strong framework with clear policies, encouraging multiple educational awareness campaigns in communities, enhancing effective cooperation among law enforcement and others, and supporting evidence-based approaches in research and application. While we recognized and assumed that pig-butchering operations with scam-related fraud are a complex problem that requires a well-rounded and coordinated response, the exact approach would depend on each country’s specific circumstances. Full article

In the food domain, text mining techniques are extensively employed to derive valuable insights from large volumes of text data, facilitating applications such as aiding food recalls, offering personalized recipes, and reinforcing food safety regulation. To provide researchers and practitioners with a comprehensive understanding of the latest technology and application scenarios of text mining in the food domain, the pertinent literature is reviewed and analyzed. Initially, the fundamental concepts, principles, and primary tasks of text mining, encompassing text categorization, sentiment analysis, and entity recognition, are elucidated. Subsequently, an analysis of diverse types of data sources within the food domain and the characteristics of text data mining is conducted, spanning social media, reviews, recipe websites, and food safety reports. Furthermore, the applications of text mining in the food domain are scrutinized from the perspective of various scenarios, including leveraging consumer food reviews and feedback to enhance product quality, providing personalized recipe recommendations based on user preferences and dietary requirements, and employing text mining for food safety and fraud monitoring. Lastly, the opportunities and challenges associated with the adoption of text mining techniques in the food domain are summarized and evaluated. In conclusion, text mining holds considerable potential for application in the food domain, thereby propelling the advancement of the food industry and upholding food safety standards. Full article

Traditional news outlets, such as newspapers and television, are no longer major sources of news. These media channels have been replaced by social platforms, which have increased in value as information distributors. This change in communication is an underlying reason for the election fraud controversies that occurred in the United States and South Korea, which hold high standards of democracy, during similar periods. This study investigates a model for sharing political disputes over social networks, especially Twitter, and illustrates the influence of political polarization. This study examines Twitter content around the presidential elections in the United States and South Korea in 2020 and 2022, respectively. It applies semantic network analysis and structural topic modeling to describe and compare the dynamics of online discourse on the issue of election fraud. The results show that online spaces such as Twitter serve as public spheres for discussion among active political participants. Social networks are key settings for forming and spreading election fraud controversies in the United States and South Korea, with differences in content. In addition, the study applies large-volume text data and new analytical methods such as the structural topic model to examine the in-depth relationships among political issues in cyberspace. Full article

Introduction: Social media has become a prevalent platform for survey dissemination, despite the paucity of literature on this topic. The purpose of this paper is to outline the benefits and drawbacks of and best practices for social media-based surveys. Methods: We performed a scoping review of this topic and explored different strategies commonly employed for conducting efficient health care surveys via social media platforms. Results: The main advantages of social media-based surveys are the convenience and flexibility of survey design, their relatively low cost, the anonymity of responders, and the ability to reach a broader population of responders across geographical boundaries. Several measures can be adopted to avoid issues inherent in this approach, such as data disruption and response duplication, as well as to enhance ethical behaviors and consent compliance. We discuss limitations associated with unclear distribution of survey respondents and outline survey fraud as a major impediment to the online propagation of surveys on various social media platforms. Discussion: The use of social media to disseminate surveys on various medical specialty topics has garnered global participation, particularly during the COVID-19 pandemic. Ethical codes of conduct emphasize the need for professionalism and truthfulness, and disclosure of potential conflicts of interest on the part of respondents, and high-quality survey research on the part of researchers. Conclusion: We advocate for the novel use of social media to promote large and diverse health care surveys. Additional studies should further explore the use of emerging social media platforms for survey dissemination and their impact on health care research. Full article

Internet usage has grown exponentially, with individuals and companies performing multiple daily transactions in cyberspace rather than in the real world. The coronavirus (COVID-19) pandemic has accelerated this process. As a result of the widespread usage of the digital environment, traditional crimes have also shifted to the digital space. Emerging technologies such as cloud computing, the Internet of Things (IoT), social media, wireless communication, and cryptocurrencies are raising security concerns in cyberspace. Recently, cyber criminals have started to use cyber attacks as a service to automate attacks and leverage their impact. Attackers exploit vulnerabilities that exist in hardware, software, and communication layers. Various types of cyber attacks include distributed denial of service (DDoS), phishing, man-in-the-middle, password, remote, privilege escalation, and malware. Due to new-generation attacks and evasion techniques, traditional protection systems such as firewalls, intrusion detection systems, antivirus software, access control lists, etc., are no longer effective in detecting these sophisticated attacks. Therefore, there is an urgent need to find innovative and more feasible solutions to prevent cyber attacks. The paper first extensively explains the main reasons for cyber attacks. Then, it reviews the most recent attacks, attack patterns, and detection techniques. Thirdly, the article discusses contemporary technical and nontechnical solutions for recognizing attacks in advance. Using trending technologies such as machine learning, deep learning, cloud platforms, big data, and blockchain can be a promising solution for current and future cyber attacks. These technological solutions may assist in detecting malware, intrusion detection, spam identification, DNS attack classification, fraud detection, recognizing hidden channels, and distinguishing advanced persistent threats. However, some promising solutions, especially machine learning and deep learning, are not resistant to evasion techniques, which must be considered when proposing solutions against intelligent cyber attacks. Full article

It is obvious that one of the most significant challenges posed by Twitter is the proliferation of fraudulent and fake accounts, as well as the challenge of identifying these accounts. As a result, the primary focus of this paper is on the identification of fraudulent accounts, fake information, and fake accounts on Twitter, in addition to the flow of content that these accounts post. The research utilized a design science methodological approach and developed a bot account referred to as “Fake Account Detector” that assists with the detection of inappropriate posts that are associated with fake accounts. To develop this detector, previously published tweets serve as the datasets for the training session. This data comes from Twitter and are obtained through the REST API. The technique of machine learning with random forest (RF) is then used to train the data. The high levels of accuracy (99.4%) obtained from the RF detection results served as the foundation for the development of the bot account. This detector tool, developed using this model, can be utilized by individuals, businesses, and government agencies to assist in the detection and prevention of Twitter problems related to fake news and fake accounts. Full article

As of 2022, non-fungible tokens, or NFTs, the smart contract powered tokens that represent ownership in a specific digital asset, have become a popular investment vehicle. In 2021, NFT trading reached USD 17.6 billion and entered mainstream media with several celebrities and major companies launching tokens within the space. The rapid rise in popularity of NFTs has brought with it a number of risks and concerns, two of which will be discussed and addressed in this technical paper. Data storage of the underlying digital asset connected to an NFT is held off-chain in most cases and is therefore out of the NFT holders’ control. This issue will be discussed and addressed using a theoretical workflow developed and presented for a system that converges NFTs and verifiable credentials with the aim of storing underlying NFT digital assets in a decentralized manner. The second issue focuses on the rise of NFT infringements and fraud within the overall NFT space. This will be discussed and addressed through the development of a practical application, named “Connect2NFT”. The main functionality of this practical application will enable users to connect their Twitter social media accounts to the NFTs they own, thus ensuring that potential buyers or viewers of the NFT can comprehensively conclude who is the authentic owner of a specific NFT. An individual performance analysis of the proposed solution will be conducted in addition to being compared and evaluated against similar applications. Thorough development, implementation, and testing has been performed in order to establish a practical solution that can be tested and applied to current NFT use cases. The theoretical NFT storage solution is a minor but equally important contribution in comparison. Full article

Social media has transformed the mode of communication globally by providing an extensive system for exchanging ideas, initiating business contracts, and proposing new professional ideas. However, there are many limitations to the use of social media, such as misinformation, lack of effective content moderation, digital piracy, data breaches, identity fraud, and fake news. In order to address these limitations, several studies have introduced the application of Blockchain technology in social media. Blockchains can provides transparency, traceability, tamper-proofing, confidentiality, security, information control, and supervision. This paper is a systematic literature review of papers covering the application of Blockchain technology in social media. To the best of our knowledge, this is the first systematic literature review that elucidates the combination of Blockchain and social media. Using several electronic databases, 42 related papers were reviewed. Our findings show that previous studies on the applications of Blockchain in social media are focused mainly on blocking fake news and enhancing data privacy. Research in this domain began in 2017. This review additionally discusses several challenges in applying Blockchain technologies in social media contexts, and proposes alternative ideas for future implementation and research. Full article

Deep learning is used to address a wide range of challenging issues including large data analysis, image processing, object detection, and autonomous control. In the same way, deep learning techniques are also used to develop software and techniques that pose a danger to privacy, democracy, and national security. Fake content in the form of images and videos using digital manipulation with artificial intelligence (AI) approaches has become widespread during the past few years. Deepfakes, in the form of audio, images, and videos, have become a major concern during the past few years. Complemented by artificial intelligence, deepfakes swap the face of one person with the other and generate hyper-realistic videos. Accompanying the speed of social media, deepfakes can immediately reach millions of people and can be very dangerous to make fake news, hoaxes, and fraud. Besides the well-known movie stars, politicians have been victims of deepfakes in the past, especially US presidents Barak Obama and Donald Trump, however, the public at large can be the target of deepfakes. To overcome the challenge of deepfake identification and mitigate its impact, large efforts have been carried out to devise novel methods to detect face manipulation. This study also discusses how to counter the threats from deepfake technology and alleviate its impact. The outcomes recommend that despite a serious threat to society, business, and political institutions, they can be combated through appropriate policies, regulation, individual actions, training, and education. In addition, the evolution of technology is desired for deepfake identification, content authentication, and deepfake prevention. Different studies have performed deepfake detection using machine learning and deep learning techniques such as support vector machine, random forest, multilayer perceptron, k-nearest neighbors, convolutional neural networks with and without long short-term memory, and other similar models. This study aims to highlight the recent research in deepfake images and video detection, such as deepfake creation, various detection algorithms on self-made datasets, and existing benchmark datasets. Full article

The impostor phenomenon (IP) (also known as impostor syndrome) describes high-achieving individuals who, despite their objective successes, fail to internalize their accomplishments and have persistent self-doubt and fear of being exposed as a fraud or impostor. This study aimed to assess the prevalence and predictors of IP within a sample of nutrition and dietetics students and practitioners. An online cross-sectional survey was conducted and utilized a non-random, convenience sampling approach. A total of 1015 students, dietetic interns, and currently practicing and retired registered dietitian nutritionists and nutrition and dietetic technicians registered provided complete responses. IP was assessed with the Clance Impostor Phenomenon Scale (CIPS). Self-reported job satisfaction and well-being were assessed using validated scales. Average CIPS score was 66.0 ± 16.3 (range 22–99), and higher scores indicate more frequent or severe IP experiences. Frequent or intense IP was reported by 64% of survey respondents (n = 655). Older age, greater educational attainment and professional level, and membership in Academy of Nutrition and Dietetics groups were associated with lower IP scores. Greater social media use was associated with higher IP scores. Job satisfaction and overall well-being were inversely correlated with IP (p < 0.001). Findings suggest that IP experiences were common among a majority of nutrition and dietetics students and practitioners surveyed. Additional research and development of preventative strategies and interventions is needed. Full article

Organizations rely heavily on audits and compliance related activities to prove their competency, credibility, and firm performance. Sustainability audits encompass entire supply chains and are very complex due to, firstly, the global nature of supply chains and, secondly, the expansive scope of sustainability, which may include financial, manufacturing, social, and environmental audits. Adding to this dilemma is the absence of a consensus on standards related to sustainability, resulting in differences, variations, and multiple interpretations. While the frequency, complexity, and scope of audits has increased, unfortunately so has the incident of audit fraud, which has seen increasing media coverage in recent times, often implicating major multinationals and their supply chains. We posit that this trend of increasing audit activity is causing “audit fatigue”, which, in turn, may influence the audit outcome, i.e., either audit fraud or a clean audit. This study proposes that audit fatigue is a genuine issue faced by organizations and needs to be conceptualized. Full article

Identifying internet spam has been a challenging problem for decades. Several solutions have succeeded to detect spam comments in social media or fraudulent emails. However, an adequate strategy for filtering messages is difficult to achieve, as these messages resemble real communications. From the Natural Language Processing (NLP) perspective, Deep Learning models are a good alternative for classifying text after being preprocessed. In particular, Long Short-Term Memory (LSTM) networks are one of the models that perform well for the binary and multi-label text classification problems. In this paper, an approach merging two different data sources, one intended for Spam in social media posts and the other for Fraud classification in emails, is presented. We designed a multi-label LSTM model and trained it on the joint datasets including text with common bigrams, extracted from each independent dataset. The experiment results show that our proposed model is capable of identifying malicious text regardless of the source. The LSTM model trained with the merged dataset outperforms the models trained independently on each dataset. Full article

The Black Friday event has become a global opportunity for marketing and companies’ strategies aimed at increasing sales. The present study aims to understand consumer behavior through the analysis of user-generated content (UGC) on social media with respect to the Black Friday 2018 offers published by the 23 largest technology companies in Spain. To this end, we analyzed Twitter-based UGC about companies’ offers using a three-step data text mining process. First, a Latent Dirichlet Allocation Model (LDA) was used to divide the sample into topics related to Black Friday. In the next step, sentiment analysis (SA) using Python was carried out to determine the feelings towards the identified topics and offers published by the companies on Twitter. Thirdly and finally, a data-text mining process called textual analysis (TA) was performed to identify insights that could help companies to improve their promotion and marketing strategies as well as to better understand the customer behavior on social media. The results show that consumers had positive perceptions of such topics as exclusive promotions (EP) and smartphones (SM); by contrast, topics such as fraud (FA), insults and noise (IN), and customer support (CS) were negatively perceived by customers. Based on these results, we offer guidelines to practitioners to improve their social media communication. Our results also have theoretical implications that can promote further research in this area. Full article