Online Deception Research Articles

Interoceptive Accuracy Enhances Deception Detection in Older Adults.

Difficulties with deception detection may leave older adults especially vulnerable to fraud. Interoception, i.e., the awareness of one's bodily signals, has been shown to influence deception detection, but this relationship has not been examined in aging yet. The present study investigated effects of interoceptive accuracy on two forms of deception detection: detecting interpersonal lies in videos and identifying text-based deception in phishing emails. Younger (18-34 years) and older (53-82 years) adults completed a heartbeat-detection task to determine interoceptive accuracy. Deception detection was assessed across two distinct, ecologically valid tasks: i) a lie detection task in which participants made veracity judgments of genuine and deceptive individuals, and ii) a phishing email detection task to capture online deception detection. Using multilevel logistic regression models, we determined the effect of interoceptive accuracy on lie and phishing detection in younger versus older adults. In older, but not younger, adults greater interoceptive accuracy was associated with better accuracy in both detecting deceptive people and phishing emails. Interoceptive accuracy was associated with both lie detection and phishing detection accuracy among older adults. Our findings identify interoceptive accuracy as a potential protective factor for fraud susceptibility, as measured through difficulty detecting deception. These results support interoceptive accuracy as a relevant factor for consideration in interventions targeted at fraud prevention among older adults.

Dark patterns: EU's regulatory efforts

AbstractIn a world where technology is rapidly advancing, regulation of dark pattern practices has become a topic of increasing importance. Society has become somewhat desensitized to these deceptive online practices that manipulate users into taking actions, which are not in their best interests, such as difficulty unsubscribing from a service, prominence of consent buttons, and countless other advanced tactics to obscure transparency. However, these ongoing practices harm both the individual user, and society in general, by impeding informed decision‐making. This Article addresses the European Union's leading efforts to tackle dark pattern practices, and in particular, addresses the numerous legislative acts which have been enacted to regulate and eliminate them. The acts explored in this Article include the General Data Protection Regulation, the Uniform Commercial Practices Directive, the Data Act, the Digital Markets Act, the Digital Services Act, the Amendment to the Directive on Financial Services Contracts Concluded at a Distance, and the Artificial Intelligence Act. This Article then discusses the interplay between the numerous acts, and the resulting ambiguities and overlap which have led to a level of regulatory redundancy. This Article examines not only the difficulty in interpretation of the various acts, but additionally, explores the issues which arise in implementation from a jurisdictional perspective. Further, this Article suggests potential solutions to address the fragmented legislation, including a hybrid form of harmonization, as well as methods for consolidation and centralization.

An exploration of the motivations of catfish perpetrators and the emotions and feelings expressed by catfish victims using automated linguistic analysis and thematic analysis

Catfishing is a form of online deception where an individual presents themselves as an identity that is not their own. The study reported in this article explored the motivations for catfish perpetrators and the impacts on those who had been catfished in terms of the emotions and feelings expressed by victims. Data was collected using the crowd-sourced question and answer website Quora [1] and analysis was conducted on a pre-existing corpus of data which contained participant’s answers to questions on catfishing. An automated linguistic analysis using Linguistic Inquiry and Word Count (LIWC-2022 [2]) and a thematic analysis (Braun and Clarke in Qual Res Psychol 3:77–101, 2006 [38]) were conducted on participant’s descriptions of their perceptions of the motivations of catfishes and their catfishing experiences. The thematic analysis indicated that the motivations of catfish perpetrators can be linked to entertainment, emulating an ideal self, desiring meaningful interaction, and financial gain. Six emotions and feelings emerged from the accounts of catfishing victims: suspicion, love, depression, anger, embarrassment, and stupidity. These findings contribute to an understanding of what motivates individuals to catfish and suggests further research to explore specific emotions and feelings that catfish victims experience. The LIWC analysis and language style matching analysis showed that the data collected was mostly personal to each individual and that there were similarities in how victims write about their experiences. Therefore, we suggest that LIWC has promise as a method of providing added context to qualitative data analysis methods.

Use of Digital Technologies: Family Perspectives on Children's Health

The increasing use of digital technologies among children and adolescents prompts concerns about their impact on development and necessitates family mediation. This study explored primary caregivers’ views on the effects of screen exposure on child health through a focus group with 20 caregivers (17 mothers and 3 grandmothers, aged 20 to 40) from an elementary school in Irecê, Bahia. Findings indicate that children’s screen time, including TV and mobile phones, often lacks proper time and content control due to caregivers’ extensive responsibilities such as remote work and household chores. Major concerns were exposure to violent games, deceptive online profiles, and potential interactions with abusers. Notable behavioral issues in children included irritability, aggression, and sleep problems. Caregivers implemented mediation strategies like setting rules, time restrictions, content filters, and discussing digital self-care. The discussion also addressed addiction mechanisms, family screen habits, gender differences in electronic device usage, and their health impacts on children. An agreement was made to limit screen use across the family, especially an hour before bedtime, aiming to enhance children’s sleep quality and thereby improve their mood and academic performance. This condensed study underscores the challenges caregivers face with children’s digital access and stresses the importance of effective mediation strategies. Enhanced parental engagement and school collaboration are crucial for fostering healthier digital habits that benefit children’s health and family well-being.

‘We’re not that Gullible!’ Revealing Dark Pattern Mental Models of 11-12 Year-Old Scottish Children

Deceptive techniques known as dark patterns specifically target online users. Children are particularly vulnerable as they might lack the skills to recognise and resist these deceptive attempts. To be effective, interventions to forewarn and forearm should build on a comprehensive understanding of children’s existing mental models. To this end, we carried out a study with 11-12 year old Scottish children to reveal their mental models of dark patterns. They were acutely aware of online deception, referring to deployers as being ‘up to no good’. Yet, they were overly vigilant and construed worst-case outcomes, with even a benign warning triggering suspicion. We recommend that rather than focusing on specific instances of dark patterns in awareness raising, interventions should prioritise improving children’s understanding of the characteristics of, and the motivations behind, deceptive online techniques. By so doing, we can help them to develop a more robust defence against these deceptive practices.

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.

Detection of Phishing Website using Machine Learning

Phishing is a widespread cybercrime where scammers trick people into sharing personal or confidential information by pretending to be a legitimate website. Despite various machine learning methods developed for detecting phishing websites using features from web samples, not much attention has been given to picking the right features efficiently. This study aims to figure out the crucial features necessary for effective phishing detection, improving the accuracy and efficiency of machine learning systems. By identifying and prioritizing these features, our research contributes to creating simpler methods that keep users safe from phishing threats. We focus on pinpointing the key characteristics that consistently set phishing websites apart from legitimate ones, enhancing the precision and reliability of phishing detection algorithms through careful analysis and experimentation. This work aligns with broader goals of strengthening cybersecurity measures, protecting individuals and organizations from falling victim to online deception, and giving users more robust tools for secure online navigation.To evaluate the feature selection in developing ageneralizable phishing detection, these classifiers are trained by a separate out-of-sample data set of 14,000 website samples. The maximum F-measure gained feature selection is 95% using Random Forest classification. Also, there are 9 universal features selected over all the three data sets. The F-measure value using this universal feature set is approximately 93% which is a comparable result in contrast to performance. Since the universal feature set contains no features from third-part services, this finding implies that with no inquiry from external sources, we can gain a fast phishing detection which is also robust toward zero-day attacks

INTEROCEPTIVE ACCURACY ENHANCES DECEPTION DETECTION WITH GREATER AGE

Abstract Financial exploitation of older adults is a growing concern, with millions of seniors scammed annually, costing billions, and resulting in devastating consequences for health, independence, and well-being. Exploitation risk is exacerbated in Alzheimer’s disease and related dementias (AD/ADRD). Detecting deception is challenging, especially for older adults who tend to believe others are truthful. Understanding the neurobiological mechanisms underlying this susceptibility is essential to design effective intervention toward reducing exploitation in aging. In this research we demonstrate the importance of interoceptive awareness─the ability to interpret body signals─in deception detection in older adults. Seventy-six young (18-34 years) and 74 older (53-82 years) adults completed a heartbeat counting task to assess their interoceptive awareness. They also engaged in deception detection paradigms across two distinct, ecologically valid tasks: i) a lie detection task in which they made veracity judgments of genuine and deceptive individuals, and ii) a phishing email detection task to capture accuracy in online deception detection. Greater interoceptive awareness was associated with greater lie detection accuracy. Furthermore, with greater chronological age among older adults, greater interoceptive awareness was associated with better accuracy in both detecting deceptive individuals and phishing emails. These findings support interoceptive awareness as a relevant factor for interventions aimed at enhancing deception detection abilities in aging. Project results will advance insight into the neurobiology of deception detection to inform effective translation into intervention in mitigating exploitation risk in aging and eventually ADRD.

People from Different Backgrounds Behave Differently in Online Deception

Online deception is a relatively new phenomenon that plays an increasingly important role in people’s daily lives. This essay mainly focuses on the comparison of similarities and differences of this phenomenon in different countries and their citizens’ attitudes. By deeply comparing people in different countries, this essay finds out that Americans prefer deception in different social media than the Chinese do. Americans prefer more realistic platforms but Chinese people prefer virtual platforms. The cultural tradition significantly impacts people’s attitudes and acceptance toward online deception, Americans show more open-mindedness and tend to believe others due to their social atmosphere, but Chinese people are more introverted and prefer to keep a mental distance from others via the internet. To deal with different cultural and societal statuses, people will show different characters in online deception. This review brings a new perspective to existing studies and inspires further studies to be deeply engaged in that field. Future research should broaden the scope of research to cover more people and groups and pay more attention to developing countries as they are rapidly changing the role that social media plays in their social structures.

Improving Social Bot Detection Through Aid and Training.

We test the effects of three aids on individuals' ability to detect social bots among Twitter personas: a bot indicator score, a training video, and a warning. Detecting social bots can prevent online deception. We use a simulated social media task to evaluate three aids. Lay participants judged whether each of 60 Twitter personas was a human or social bot in a simulated online environment, using agreement between three machine learning algorithms to estimate the probability of each persona being a bot. Experiment 1 compared a control group and two intervention groups, one provided a bot indicator score for each tweet; the other provided a warning about social bots. Experiment 2 compared a control group and two intervention groups, one receiving the bot indicator scores and the other a training video, focused on heuristics for identifying social bots. The bot indicator score intervention improved predictive performance and reduced overconfidence in both experiments. The training video was also effective, although somewhat less so. The warning had no effect. Participants rarely reported willingness to share content for a persona that they labeled as a bot, even when they agreed with it. Informative interventions improved social bot detection; warning alone did not. We offer an experimental testbed and methodology that can be used to evaluate and refine interventions designed to reduce vulnerability to social bots. We show the value of two interventions that could be applied in many settings.

What makes deceptive online reviews? A linguistic analysis perspective

AbstractWith the rapid development of e-commerce, online reviews have become an important information source for consumers and e-commerce businesses. While the negative impact of deceptive online reviews has been well recognized, more research has to be done to help understand the linguistic manifestations of deceptive online reviews in order to help identify deceptive reviews and help increase the value and sustainability of e-commerce businesses. This study explores the linguistic manifestations of deceptive online reviews based on the reality monitoring theory, and then uses the data from Amazon.com online product reviews to examine perceptual cues, affective cues, detail cues, relevance cues, and cognitive cues of various deceptive online reviews. The results show that reviews for emotional catharsis are more extreme with affective cues, while perfunctory reviews often lack details with fewer prepositions and adjectives. In addition, deceptive reviews often lack relevance cues when these reviews are made to obtain the rewards provided by the vendors while paid posters tend to use more cognitive cues in deceptive reviews. Moreover, deceptive online reviews under all motives often lack perceptual cues. These findings provide a deeper understanding of the linguistic manifestations of deceptive online reviews and provide significant managerial implications for e-commerce businesses to employ high-quality online reviews for sustainable growth.

Gender differences in the relationship between attachment styles, self-esteem and online deception: A mediation model

Online Deception is growing as a topic of research due to its potential risks for mental health and behaviors. This study explored the relationships between attachment styles and intentional misrepresentation of oneself in online dating (i.e., online deception) and investigated the mediating role of self-esteem. A cross-sectional online survey was conducted with voluntary Italian participants (N = 272) to test the proposed hypotheses. It was found that anxious attachment styles positively predicted online deception. Also, self-esteem was found to be mediator between anxious attachment styles and online deception, as well as between close attachment styles and online deception. Our results demonstrate the need to develop prevention interventions that target individuals’ styles of attachment that in turn foster self-esteem which co-contribute to promote a sensible and healthy use of online dating.

Awareness of Deceptive Product Reviews and its Impact on Purchasing Intentions among University Youth: A Survey Study (Arabic)

Deceptive reviews are increasingly prevalent online. Considering existing literature, there are not sufficient research studies that seek to understand the role of deceptive product reviews in affecting acts of online purchasing in the Arab world. This paper attempts to investigate the perception of university youth toward deceptive online product reviews and their impact on purchasing intentions, as well as the reputational impact on the companies that offer the products and websites that carry deceptive reviews. The study followed an approach devoted to monitoring the attitudes of university youth toward review credibility through a questionnaire. Participants were requested to read reviews of two familiar items; the iPhone and a hotel. One symbolized a product, while the other denoted an experience. Participants were requested to imagine buying a mobile phone and booking a hotel in light of a range of reviews adequately perceived before moving on to answer the questions raised. The sample size was 400, representing three universities. The study found that university youth were unable to recognize fallacious reviews. Hence, there was an impact on their purchasing intentions, besides having a recognizable effect on the reputation of brands and review sites. This article is beneficial to inform youth about the importance of critical decision making in cases of fallacious information. The study also represented a reference for marketing agents to choose appropriate techniques for effective advertising. Moreover, it supports the development of online review mechanisms, particularly for authorized Saudi bodies in charge of electronic commerce regulations.

The effect of online deception on behavioral intention towards e-tailers: the mediating role of e-shopping value and e-trust

ABSTRACT This research aims to establish the impact of online deception and e-security on behavioural intentions towards e-retailers in Saudi Arabia, as well as highlight the mediating effects of online shopping’s perceived value and e-trust. An electronic survey was distributed through email and social media to Saudi consumers, resulting in 216 valid responses from e-shopping adopters. The emergent relationships between the variables were identified, and some managerial implications were proposed. The originality of this work stems from how it tests the impact of perceived online deception and e-security on e-trust, online shopping value, and behavioural intentions. E-trust and online shopping value were positioned, and subsequently verified, as mediators between the proposed dependent and independent variables. The results reveal that trust towards e-retailers plays a mediating role in the relationship between online deception and perceived online shopping value. Furthermore, the indirect impact of trust towards an e-retailer on behavioural intentions is also partially mediated by online shopping’s perceived value.

Online deceptive advertising and consumer protection in South Africa - The law and its shortcomings?

E-commerce and e-marketing has grown significantly over the past few years. More businesses are moving away from original forms of marketing tools such as newspapers, magazines, billboards and televisions and instead prefer online platforms such as social media. The article focuses on false, misleading and deceptive online marketing representations. It examines the legislative framework that seeks to protect online consumers in South Africa in terms of the Electronic Communications and Transactions Act 25 of 2002 (ECTA), the Consumer Protection Act 68 of 2008 (CPA) as well as the Social Media Code1 of the Advertising Regulatory Board (ARB). It is recommended that although a consolidated statute that makes provision for both offline and online consumers would be ideal, the current provisions in ECTA could also be reviewed to ensure that they are in line with the new developments in marketing trends such as influencer marketing. The Social Code of the ARB is also important to complement ECTA and the CPA, as well as to promote the overall protection of consumers in South Africa.

World wide web of lies: Personality and online deception

The deliberate act of falsifying aspects of a person's identity is rampant online and across social networking sites. Although personality traits have been found to be related to deception in traditional contexts, less is known about how this relationship translates to an online setting. The current study investigates the relationships between the dark tetrad traits (Machiavellianism, psychopathy, narcissism, and sadism), self-esteem, and online identity-based deception. A combined student and community sample (N = 342, 18 – 72 years old, 70.2% female) was asked if they had previously engaged in eight common identity-based deceptive behaviours on social networking sites (e.g., used a fake name, created a fake profile). Personality traits were measured using the Short Dark Triad, the Short Sadistic Impulse Scale, and the Rosenburg Self-Esteem Scale. Overall, engagement in identity-based deception was found to be common on social networking sites. Machiavellianism emerged as a unique predictor of using a fake name and altering one's age with higher Machiavellianism also associated with a greater frequency of lying about one's name, age, appearance, and relationship status. Psychopathy, however, failed to uniquely predict any of the online-deceptive behaviours examined when controlling for other personality variables. Together, these findings suggest that online identity-based deception is a calculated endeavour.

Slimy tactics: the covert commercialisation of child-targeted content

ABSTRACT There are growing concerns about the commercialisation of childhood and the consequences of marketing to children, including marketing’s negative effects and increasingly sophisticated and potentially deceptive online tactics that permeate the contemporary media environment. Children and young people are prolific users of video-sharing platforms (VSP) such as TikTok and YouTube, yet little is known about children’s advertising literacy within these contexts. Therefore, this paper answers recent calls for a deeper understanding of children’s advertising literacy in light of their increasingly digital lifestyles. A combination of qualitative, participatory creative methods were used with children aged 9–11 from the UK. Findings show that despite their negative perceptions of online advertising, children place a level of trust in video-sharing platforms, based on a misconception that the commercialisation of content is always disclosed. This is concerning because children may prefer and pay more attention to content that they believe to be genuine.

Who Gets Caught in the Web of Lies?: Understanding Susceptibility to Phishing Emails, Fake News Headlines, and Scam Text Messages.

The present study investigated if the same users are vulnerable to phishing emails, scam text messages, and fake news headlines and if there are universal predictors of susceptibility for all three tasks. Theoretical research provides support for the notion that the same users likely fall for multiple forms of online deception. However, no research has directly compared susceptibility for various online deceptions (eg phishing, disinformation, scam text messages) within the same group of users. Participants completed an online survey consisting of demographic questions, the Cognitive Reflection Test (ie impulsivity), and the Digital Literacy Scale, and classified 90 legitimate and deceptive emails, text messages, and news headlines. Results suggest that individuals who struggle to discriminate between deceptive and legitimate stimuli on one task experience similar difficulties on the other two tasks. Additionally, while lower levels of digital literacy and cognitive reflectiveness predicted poorer discrimination abilities across all three tasks, age did not predict performance. Interestingly, participants appeared to be the most susceptible to phishing emails. Overall, individuals who fall for one form of online deception appear to be more likely to fall for other forms of deception, and digital literacy and cognitive reflectiveness can predict widespread vulnerability to online deception. Organizations may be able to identify potential vulnerabilities for a variety of online attacks by measuring digital literacy, cognitive reflectiveness, and performance in one online deception task. Additionally, training interventions may be the most needed for phishing emails.

Deep learning for classifying thai deceptive messages

Online deception has become a major problem affecting people, society, the economy, and national security. It is mostly done by spreading deceptive messages because message are quickly spread on social networks and are easily accessed by anyone. Detecting deceptive messages is challenging as the messages are unstructured, informal, and complex; this extends into Thai language messages. In this paper, various deep learning models are proposed to detect deceptive messages under two feature extraction trials. A balanced two-class dataset of deceptive and truthful Thai messages (n=2378) is collected from Facebook pages. Instance features are encoded using word embeddings (Thai2Fit) and one-hot encoding techniques. Five classification models, convolutional neural network (CNN), bidirectional long short-term memory (BiLSTM), bidirectional gated recurrent units (BiGRU), CNN-BiLSTM, and CNN-BiGRU, are proposed and evaluated upon the dataset with each feature extraction technique. The experimental results show that all the proposed models had excellent accuracy (95.59% to 98.74%) and BiLSTM with one-hot encoding gave the best performance, achieving 98.74% accuracy.

Dark Trait Tetrad and Online Deception in Tiktok users

The research aimed to assess the relationship between dark trait tetrad and online deception in TikTok users. It was hypothesized that there is a significant positive relationship between dark tetrad and online deception. Furthermore, the dark tetrad may significantly predict online deception in TikTok users. Moreover, there is a gender difference in dark tetrad and online deception in TikTok users. Correlational research design and Purposive sampling technique were used to collect data. The sample comprised N = 100 TikTok users constituting both Men (n = 50) and Women (n = 50). Dark Tetrad Traits Scale, Measuring of Online Deception and Intimacy Scale (MODI) was used to assess the targeted study variables in TikTok users. The results revealed that there is a significant positive relationship between Dark Tetrad and Online Deception among TikTok users. The narcissism and sadism subscale of Dark Tetrad came out to be positive predictors of Online Deception in TikTok users. While the result of the independent sample t-test indicated non-significant gender differences in Dark Trait Tetrad and Online Deception. This study will help provide the basis for further empirical research, about the etiological reason for antisocial behavior.