Deceptive Tactics Research Articles

Unveiling deceptive tactics: exploring fraudulent online ratings in the restaurant industry and their impact on consumer choice

Unveiling deceptive tactics: exploring fraudulent online ratings in the restaurant industry and their impact on consumer choice

The law of perfidy and ruses of war at sea

Abstract The principle of distinction is a fundamental principle in the law of armed conflict, aimed at protecting civilians and civilian objects from direct attacks. Adherence to this principle is crucial for minimizing the humanitarian impact of warfare. Nevertheless, the longstanding practice of ruses of war, deeply embedded in customary international law, complicates the application of this principle in combat. While certain deceptive tactics are allowed, the line between acceptable ruses and forbidden acts of perfidy is often unclear, especially in naval warfare. The evolution of modern warfare technology further obscures these distinctions, raising important legal questions regarding their relevance today. This article critically examines the existing legal framework governing ruses and perfidy at sea and explores how these rules apply to contemporary naval operations. Finally, the article identifies key legal gaps and proposes pathways for reform to better align the law with current realities.

Strategic investment in charging infrastructure: Sharing costs or taking over?

The substantial upfront investment required for the installation of charging infrastructure deters many potential charging station adopters, such as shopping mall operators and real estate owners, from independently undertaking the deployment. Some government agencies (Govs) offer subsidies as a means of encouraging the extensive implementation of charging infrastructure. Nonetheless, this approach is not viable in the long term owing to the constrained public budget. An improper subsidy scheme may induce adopters to become overly reliant on Govs support. To tackle this issue, some local Govs have initiated cost-sharing schemes with adopters who are willing to install chargers. In a typical cost-sharing scheme, the Gov initially assigns the cost-sharing rate to adopters. Subsequently, the adopters determine the quantity they are willing to install through cost-sharing. Despite the potential benefits of the scheme, there are still some challenges: (i) The public budget is limited, (ii) The charging infrastructure penetration goal is uncertain, (iii) Adopters are heterogeneous and their decisions are interdependent, and (iv) Adopters possess dissimilar beliefs regarding the policy of the Gov. These factors could potentially prompt certain adopters, who possess the financial capability to cover the installation expenses, to employ deceptive tactics aimed at persuading the government to assume the entire cost. In consideration of the aforementioned challenges, we propose a sequential-move game model to assess the strategic interactions between the Gov and charging station adopters. We demonstrate that there exists a many-to-one mapping between the optimal cost-sharing rates and the optimal number of installed chargers. Then, we derive the conditions when the Gov withdraws from the market and when it intervenes to share costs with adopters. The results suggest that a reduction in the level of competition intensity leads to an increased probability of adopters engaging in the cost-sharing scheme. This is in contrast to scenarios where policy uncertainty increases or where the correlative effects arise among all adopters’ strategies.

Fake review detection with label-consistent and hierarchical-relation-aware graph contrastive learning

The rapid increase in fake reviews in e-commerce presents a considerable challenge, as it misleads consumers and compromises market integrity. Recently, graph-based models for detecting fake reviews have emerged as promising solutions. However, conventional methods and basic graph neural network techniques often fail to recognize the deceptive tactics of fraudsters, negatively affecting their efficacy. In response, we propose the label-consistent and hierarchical-relation-aware graph contrastive learning (LRGCL) framework, which is specifically designed to identify fake reviews, even when fraudsters employ various disguises. This framework implements a label-consistent graph contrastive learning approach to distinctly identify positive and negative samples, ensuring clear separation between authentic and fraudulent nodes despite feature camouflage. Additionally, LRGCL classifies negative samples into several categories using a hierarchical approach that leverages relationships within a multi-relation graph, thereby counteracting relation camouflage. The integration of these two strategies significantly reduces the effectiveness of these camouflages, leading to more precise detection of fake reviews. Empirical tests using real-world datasets validate the superior efficacy of LRGCL, as it surpasses current leading methods across key performance metrics.

The dark side of publishing: Unveiling the deceptive tactics of cloned journals in India

The study examines two lists of cloned journals (Group I and Group II) compiled by the University Grants Commission-Consortium for Academic and Research Ethics (UGC-CARE) in India. India has emerged as a hotspot for predatory publishing, justifying the reliance on the UGC-CARE list. The study explores the role of language as a tool for cloning legitimate journals and reveals that cloned journals adopt the English language to deceive and attract submissions. Additionally, it investigates the techniques used by cloned journals to modify their appearance and nomenclature. The results indicate that 40.47% of cloned journals maintain the original title, 42.86% make minor changes by adding terms like ‘international’ and ‘journal’, and 16.67% undergo major alterations. The study also examines the permanence and stability of the domains associated with cloned journals, finding that 52.50% of URLs were active and a significant number were inactive. Among these, the highest domain names include .com (commercial), followed by.org (organisation), as cloned journals prefer these to appear more credible and trustworthy. Furthermore, the study analyses the subject coverage of cloned journals to understand whether their focus is on specific disciplines. The findings reveal that they adopt a multidisciplinary approach to their deceptive practices. By providing valuable insights and raising awareness of how cloned journals deceive authors, this study serves as a significant step against these fraudulent practices. The main limitation of this study is that it relies on a single data source, the UGC CARE List India.

Does the Buck Stop Somewhere? Blame Games and State Capture in the Legalization of the Israeli Land Grab

This article explores the intricate relationship between the Israeli state and Jewish settlers in the occupied Palestinian West Bank after the Oslo Accords (1993). It shows that, despite the accords, which prohibited the establishment of new settlements, such activities persisted. Instead, the accords triggered the seemingly spontaneous emergence of numerous illegal “hilltop settlements,” tolerated by higher echelons of government and actively supported by lower levels. These settlements subsequently underwent a gradual post hoc formalization process, ultimately leading to their full legalization. The article elucidates how a faction within Israeli society influenced segments of the government, employing blame games as a strategy to rationalize the legitimization of the Israeli land grab. Notably, it highlights the strategic and deceptive tactics employed by various Israeli governments in their dealings with the settler movement. The article concludes by reflecting on the political implications of the settler movement's success for the democratic character of the Israeli state.

SEXUAL DECEPTION USED BY INDIVIDUALS TO INITIATE SEXUAL ENGAGEMENT WITH THEIR CURRENT OR POTENTIAL PARTNER

Sexual deception, the deliberate manipulation of information regarding one’s sexual identity, preferences, and intentions, is a complex phenomenon prevalent in intimate relationships. This research aims to delve into the intricate dynamics of sexual deceit, exploring the connection between male and female perspectives in sexual relationships. The study delves into the reasons behind sexual deception, examining why males are perceived as more manipulative compared to females and how gender influences deceptive practices in sexual contexts. The paper posits that individuals utilize deceptive tactics, including sexual deception, to enhance intimacy in relationships, driven by desires for gratification, fear of rejection, and the pursuit of personal goals. Utilizing the Sexual Deception Scale developed by Marelich et al. (2008), the research uncovers insights into the different dimensions of sexual deception, including blatant lying, self-serving tactics, and avoiding confrontation. Physical beauty plays a major role in sexual deception. Lack of sex in life can cause problems like isolation, self- doubt and trust. Through a comprehensive analysis of the scale’s subscales and total scores, the study sheds light on the prevalence of deceptive practices in intimate relationships and the gender disparities in manipulative behaviors.

Deceptive tactics: Misappropriation of scientific literature by ‘Gazeta do Povo’ in undermining COVID-19 vaccination efforts

Deceptive tactics: Misappropriation of scientific literature by ‘Gazeta do Povo’ in undermining COVID-19 vaccination efforts

Investigating impression management use in asynchronous video interviews across 10 countries

AbstractThis cross‐cultural study investigates how interviewees from 10 culturally‐distinct countries differ in their use of impression management (IM) tactics in asynchronous video interviews (AVIs), and the relationship(s) between those tactics and interview performance. A total of 582 participants from ten countries (India, Canada, South Africa, Poland, Spain, Iran, Germany, Chile, Philippines, China) completed an 8‐question AVI for a mock position as a manager in a bank. We drew upon GLOBE's cultural framework to predict and explain observed differences in self‐reported IM use and performance. We used multi‐level modeling to test our hypotheses. Interviewees from our ten countries differed slightly in their IM use for various tactics, but IM use was seldom related to GLOBE cultural dimensions. Partially consistent with previous in‐person interview research, honest IM tactics (e.g., self promotion) were positively, but deceptive tactics (e.g., extensive image creation) negatively, associated with interview performance. This research is the first to investigate cross‐cultural IM differences in AVIs, thus addressing a critical gap in the selection literature at a time when many organizations conduct interviews virtually to save costs, streamline the hiring process, or simply conduct most of their activities remotely.

Advanced Algorithmic Approaches for Scam Profile Detection on Instagram

Social media platforms like Instagram have become a haven for online scams, employing various deceptive tactics to exploit unsuspecting users. This paper investigates advanced algorithmic approaches to combat this growing threat. We explore various machine learning models for scam profile detection on Instagram. Our methodology involves collecting a comprehensive dataset from a trusted source and meticulously preprocessing the data for analysis. We then evaluate the effectiveness of a suite of machine learning algorithms, including decision trees, logistic regression, SVMs, and other ensemble methods. Each model’s performance is measured using established metrics like accuracy, precision, recall, and F1-scores. Our findings indicate that ensemble methods, particularly random forest, XGBoost, and gradient boosting, outperform other models, achieving accuracy of 90%. The insights garnered from this study contribute significantly to the body of knowledge in social media forensics, offering practical implications for the development of automated tools to combat online deception.

Phish Catcher: Client-Side Defense against Web-Spoofing Attacks Using Machine Learning

Cybersecurity faces a significant challenge in safeguarding users' confidential information, such as passwords and PIN codes, from phishing attacks. These attacks, which employ various deceptive tactics like fake login pages, phishing emails, and click-jacking, aim to trick users into divulging sensitive data. Traditional security strategies often encounter issues of latency and accuracy in detecting these fraudulent activities. To address this challenge, we propose a client-side defense mechanism leveraging machine learning techniques to detect spoofed web pages and protect users from phishing attacks. In this work, we introduce Phish Catcher, a Google Chrome extension developed as a proof of concept for our machine learning-based approach. Phish Catcher utilizes a random forest classifier trained on four types of web features to classify URLs as suspicious or trustworthy. To evaluate the effectiveness of our extension, we conducted experiments on real web applications, testing 400 classified phishing URLs and 400 legitimate URLs. The results demonstrate a remarkable accuracy and precision of 98.5% for detecting spoofed web pages. Furthermore, we assessed the latency of Phish Catcher by measuring its response time over forty phished URLs. The average recorded response time was just 62.5 milliseconds, indicating minimal impact on user experience while providing robust protection against phishing attacks. Overall, our approach offers a highly accurate and efficient solution for detecting phishing attempts, thereby enhancing user security and mitigating the risks associated with online fraud.

The false waiver: a deceptive tactic of predatory medical journals

The false waiver: a deceptive tactic of predatory medical journals

Phish Catcher: Client-Side Defense against Web-Spoofing Attacks Using Machine Learning

Cybersecurity faces a significant challenge in safeguarding users' confidential information, such as passwords and PIN codes, from phishing attacks. These attacks, which employ various deceptive tactics like fake login pages, phishing emails, and click-jacking, aim to trick users into divulging sensitive data. Traditional security strategies often encounter issues of latency and accuracy in detecting these fraudulent activities. To address this challenge, we propose a client-side defense mechanism leveraging machine learning techniques to detect spoofed web pages and protect users from phishing attacks. In this work, we introduce Phish Catcher, a Google Chrome extension developed as a proof of concept for our machine learning-based approach. Phish Catcher utilizes a random forest classifier trained on four types of web features to classify URLs as suspicious or trustworthy. To evaluate the effectiveness of our extension, we conducted experiments on real web applications, testing 400 classified phishing URLs and 400 legitimate URLs. The results demonstrate a remarkable accuracy and precision of 98.5% for detecting spoofed web pages. Furthermore, we assessed the latency of Phish Catcher by measuring its response time over forty phished URLs. The average recorded response time was just 62.5 milliseconds, indicating minimal impact on user experience while providing robust protection against phishing attacks. Overall, our approach offers a highly accurate and efficient solution for detecting phishing attempts, thereby enhancing user security and mitigating the risks associated with online fraud.

Analysis of Japanese Defeats at The Battle of Midway in 1942

Analysis of Japanese Defeats at The Battle of Midway in 1942

The Language of Lies: An Analysis of Deceptive Linguistic Cues on Malaysian Investors’ Decision Making

In our technology-driven era, scams have become an increasingly pervasive threat, impacting countless individuals daily and causing not only financial losses but also emotional distress and a lack of trust in societal systems. Among the plethora of fraudulent activities, investment scams stand out as one of the top three reported types. These scams often involve scammers gaining the trust of victims through investment tutoring before soliciting money. This qualitative study aims to analyse the linguistic cues of deception embedded in investment scams’ promotional materials. The significance of this research lies in its focus on linguistic cues that utilise considerable influence over investment decision-making processes. The study used linguistic analysis to examine investment scam webpages from reputable organisations like the Securities Commission Malaysia (SC), the Central Bank of Malaysia (CBM), the Financial Consumer Alert List (FCA), and the Royal Malaysia Police (RMP). The qualitative analysis revealed different linguistic cues used, such as credibility enhancement, expressive language, cognitive biases, and framing effects. These findings illuminate how scammers manipulate language to shape investors’ perceptions of promotional materials. By offering insights into these deceptive tactics, the study equips individuals with the knowledge necessary to navigate financial environments with greater awareness and resilience, thereby serving as a crucial tool in the ongoing battle against investment fraud.

Implicacions als tuits dels escèptics del canvi climàtic

The article attempts to provide a speech act analysis of tweets which are posted by climate change skeptics. It argues that their deliberate flouting and violation of Grice’s Cooperative Principle show that substitute overt denial by conversational implicatures which show skepticism. Based on data from 420 tweets which are responses to climate change discussion, the paper demonstrates that tweeters respond with different deceptive tactics by using conversational implicatures. The article attempts to provide a pragmatic analysis of tweets which are posted by climate change opponents. It argues that their deliberate violation of the CP shows that their attitude shifted from overt denial to skepticism.

Revealing deceptive tactics: uncovering predatory practices within the "Global International Journal of Innovative Research"

In the pursuit of academic excellence, scholars are dependent on trustworthy journals to disseminate their research and contribute to the collective body of knowledge. However, the academic community is not immune to deceptive practices. This letter highlights the case of the "Global International Journal of Innovative Research," where my name, along with esteemed colleagues, was falsely associated with the editorial board without consent. Despite repeated efforts to address this issue, the journal remains unresponsive. This letter announces our intention to publish an exposé article, meticulously uncovering the fraudulent practices of the journal and its publisher. A subsequent public statement aims to warn the academic community about the deceptive nature of the "Global International Journal of Innovative Research" and emphasizes the collective responsibility of scholars to uphold ethical publishing standards. Through this exposé, we hope to safeguard the integrity of academic pursuit and raise awareness about the risks associated with engaging with predatory journals.

Deceptive XAI: Typology, Creation and Detection

Providing rationales for decisions can enhance transparency and cultivate trust. Nevertheless, in light of economic incentives and other factors that may encourage manipulation, the reliability of such explanations comes into question. This manuscript builds upon a previous conference paper∗\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^*$$\\end{document} by introducing a conceptual framework for deceptive explanations and constructing a typology grounded in interdisciplinary literature. The focus of our work is on how AI models can generate and detect deceptive explanations. In our empirical evaluation, we focus on text classification and introduce modifications to the explanations generated by GradCAM, a well-established method for explaining neural networks. Through a user study comprising 200 participants, we demonstrate that these deceptive explanations have the potential to mislead individuals. However, we also demonstrate that machine learning (ML) techniques can discern even subtle deceptive tactics with an accuracy exceeding 80%, given sufficient domain expertise. Furthermore, even in the absence of domain knowledge, unsupervised learning can be employed to identify inconsistencies in the explanations, provided that fundamental information about the underlying predictive model is accessible.

‘DON’T FOOL ME, AS IT IS YOUR LOSS’ - IMPACT OF DECEPTION ON INFORMATION PRIVACY

ABSTRACTWith the explosion of e-commerce, the potential for new internet portals to mislead or fool consumers has grown significantly. This study investigates the influence of these deceptive actions on customer privacy and their information-sharing behavior. We have used online experimental research to demonstrate the influence of deception on information privacy and information-sharing behavior. We demonstrate that misleading activities such as falsification, concealment and equivocation reduce customers’ trust in the platform and increase their privacy concerns, resulting in a lower willingness to disclose any information on the platforms. We further explain the impact of several types of deceptive tactics in the online purchasing environment and present a mediation model to understand the impact of deception. The study has implications for category managers and consumers of e-commerce platforms.

What Eye Tracking Can Show Us About How People Are Influenced by Deceptive Tactics in Line Graphs

What Eye Tracking Can Show Us About How People Are Influenced by Deceptive Tactics in Line Graphs