Explore the vast range of titles available.

Computer-mediated deception threatens the security of online users' private and personal information. Previous research confirms that humans are bad lie detectors, while demonstrating that certain observable linguistic features can provide crucial cues to detect deception. We designed and conducted an experiment that creates spontaneous deception scenarios in an interactive online game environment. Logistic regression, and certain classification methodologies were applied to analyzing data collected during fall 2014 through spring 2015. Our findings suggest that certain language-action cues (e.g., cognitive load, affective process, latency, and wordiness) reveal patterns of information behavior manifested by deceivers in spontaneous online communication. Moreover, computational approaches to analyzing these language-action cues can provide significant accuracy in detecting computer-mediated deception.

Phishing has been a major problem for information systems managers and users for several years now. In 2008, it was estimated that phishing resulted in close to $50 billion in damages to U.S. consumers and businesses. Even so, research has yet to explore many of the reasons why Internet users continue to be exploited. The goal of this paper is to better understand the behavioral factors that may increase one's susceptibility for complying with a phisher's request for personal information. Using past research on deception detection, a research model was developed to help explain compliant phishing responses. The model was tested using a field study in which each participant received a phishing e-mail asking for sensitive information. It was found that four behavioral factors were influential as to whether the phishing e-mails were answered with sensitive information. The paper concludes by suggesting that the behavioral aspect of susceptible users be integrated into the current tools and materials used in antiphishing efforts.

Artificial Intelligence (AI) technologies have been progressing constantly and being more visible in different aspects of our lives. One recent phenomenon is ChatGPT, a chatbot with a conversational artificial intelligence interface that was developed by OpenAI. As one of the most advanced artificial intelligence applications, ChatGPT has drawn much public attention across the globe. In this regard, this study examines ChatGPT in education, among early adopters, through a qualitative instrumental case study. Conducted in three stages, the first stage of the study reveals that the public discourse in social media is generally positive and there is enthusiasm regarding its use in educational settings. However, there are also voices who are approaching cautiously using ChatGPT in educational settings. The second stage of the study examines the case of ChatGPT through lenses of educational transformation, response quality, usefulness, personality and emotion, and ethics. In the third and final stage of the study, the investigation of user experiences through ten educational scenarios revealed various issues, including cheating, honesty and truthfulness of ChatGPT, privacy misleading, and manipulation. The findings of this study provide several research directions that should be considered to ensure a safe and responsible adoption of chatbots, specifically ChatGPT, in education.

Safeguarding organizations against opportunism and severe deception in computer-mediated communication (CMC) presents a major challenge to chief information officers and information technology managers. New insights into linguistic cues of deception derive from the speech acts innate to CMC. Applying automated text analysis to archival e-mail exchanges in a CMC system as part of a reward program, we assess the ability of word use (micro level), message development (macro level), and intertextual exchange cues (meta level) to detect severe deception by business partners. We empirically assess the predictive ability of our framework using an ordinal multilevel regression model. Results indicate that deceivers minimize the use of referencing and self-deprecation but include more superfluous descriptions and flattery. Deceitful channel partners also overstructure their arguments and rapidly mimic the linguistic style of the account manager across dyadic e-mail exchanges. Thanks to its diagnostic value, the proposed framework can support firms' decision making and guide compliance monitoring system development.

As the world “gets smaller” and more people engage in cross-cultural communications, their ability to successfully separate truth from deception can be critically important. Yet it is challenging. While deceptive communication has been studied for decades, some areas are not well understood. In particular, two areas that could benefit from further research concern the effects of cultural differences and communication media on deception and its detection. Building on developments in theories of deception and its detection, we examine the question: How do differences in culture between senders and receivers affect deception detection, especially where the deceptive communication occurs across different media? To address this question, stimulus materials from recorded interviews were created featuring participants from the United States, Spain, and India. Three stimulus sets were created, one each in American English, Spanish, and Indian English, and each consisting of 32 interview snippets. Half of the snippets were honest and half were dishonest. Each snippet represented one of four media: full audio-visual, video only, audio only, and text only. Veracity judges were also recruited from the same three countries as the interview participants, to independently observe and evaluate the communication both within their culture and across other cultures. Evidence was found that different combinations of cultural and media effects affected the accuracy of deception detection.

Purpose With the widespread use of online communications, users are extremely vulnerable to a myriad of deception attempts. This study aims to extend the literature on deception in computer-mediated communication by investigating whether the manner in which popularity information (PI) is presented and media richness affects users’ judgments. Design/methodology/approach This study developed a randomized, within and 2 × 3 between-subject experimental design. This study analyzed the main effects of PI and media richness on the imitation magnitude of veracity judges and the effect of the interaction between PI and media richness on the imitation magnitude of veracity judges. Findings The manner in which PI is presented to people affects their tendency to imitate others. Media richness also has a main effect; text-only messages resulted in greater imitation magnitude than those viewed in full audiovisual format. The findings showed an interaction effect between PI and media richness. Originality/value The findings of this study contribute to the information systems literature by introducing the notion of herd behavior to judgments of truthfulness and deception. Also, the medium over which PI was presented significantly impacted the magnitude of imitation tendency: PI delivered through text-only medium led to a greater extent of imitation than when delivered in full audiovisual format. This suggests that media richness alters the degree of imitating others’ decisions such that the leaner the medium, the greater the expected extent of imitation.

Deception in computer-mediated communication represents a threat, and there is a growing need to develop efficient methods of detecting it. Machine learning models have, through natural language processing, proven to be extremely successful at detecting lexical patterns related to deception. In this study, four selected machine learning models are trained and tested on data collected through a crowdsourcing platform on the topics of COVID-19 and climate change. The performance of the models was tested by analyzing n-grams (from unigrams to trigrams) and by using psycho-linguistic analysis. A selection of important features was carried out and further deepened with additional testing of the models on different subsets of the obtained features. This study concludes that the subjectivity of the collected data greatly affects the detection of hidden linguistic features of deception. The psycho-linguistic analysis alone and in combination with n-grams achieves better classification results than an n-gram analysis while testing the models on own data, but also while examining the possibility of generalization, especially on trigrams where the combined approach achieves a notably higher accuracy of up to 16%. The n-gram analysis proved to be a more robust method during the testing of the mutual applicability of the models while psycho-linguistic analysis remained most inflexible.

The increased chance of deception in computer-mediated communication and the potential risk of taking action based on deceptive information calls for automatic detection of deception. To achieve the ultimate goal of automatic prediction of deception, we selected four common classification methods and empirically compared their performance in predicting deception. The deception and truth data were collected during two experimental studies. The results suggest that all of the four methods were promising for predicting deception with cues to deception. Among them, neural networks exhibited consistent performance and were robust across test settings. The comparisons also highlighted the importance of selecting important input variables and removing noise in an attempt to enhance the performance of classification methods. The selected cues offer both methodological and theoretical contributions to the body of deception and information systems research.

To further cybersecurity, there is interest in studying online cybercriminal communities to learn more about emerging cyber threats. Literature documents the existence of many online Internet Relay Chat (IRC) cybercriminal communities where cybercriminals congregate and share hacking tools, malware, and more. However, many cybercriminal community participants appear unskilled and have fleeting interests, making it difficult to detect potential long-term or key participants. This is a challenge for researchers and practitioners to quickly identify cybercriminals that may provide credible threat intelligence. Thus, we propose a computational approach to analyze cybercriminals IRC communities in order to identify potential long-term and key participants. We use the extended Cox model to scrutinize cybercriminal IRC participation for better understanding of behaviors exhibited by cybercriminals of importance. Results indicate that key cybercriminals may be quickly identifiable by assessing the scale of their interaction and networks with other participants.

Automatic deception detection is a crucial task that has many applications both in direct physical and in computer-mediated human communication. Our focus is on automatic deception detection in text across cultures. In this context, we view culture through the prism of the individualism/collectivism dimension, and we approximate culture by using country as a proxy. Having as a starting point recent conclusions drawn from the social psychology discipline, we explore if differences in the usage of specific linguistic features of deception across cultures can be confirmed and attributed to cultural norms in respect to the individualism/collectivism divide. In addition, we investigate if a universal feature set for cross-cultural text deception detection tasks exists. We evaluate the predictive power of different feature sets and approaches. We create culture/language-aware classifiers by experimenting with a wide range of n-gram features from several levels of linguistic analysis, namely phonology, morphology and syntax, other linguistic cues like word and phoneme counts, pronouns use, etc., and token embeddings. We conducted our experiments over eleven data sets from five languages (English, Dutch, Russian, Spanish, and Romanian), from six countries (United States of America, Belgium, India, Russia, Mexico, and Romania), and we applied two classification methods, namely logistic regression and fine-tuned BERT models. The results showed that the undertaken task is fairly complex and demanding. Furthermore, there are indications that some linguistic cues of deception have cultural origins and are consistent in the context of diverse domains and data set settings for the same language. This is more evident for the usage of pronouns and the expression of sentiment in deceptive language. The results of this work show that the automatic deception detection across cultures and languages cannot be handled in unified manners and that such approaches should be augmented with knowledge about cultural differences and the domains of interest.