Deception In Computer-mediated Communication Research Articles

Detecting deception in computer-mediated communication: the role of popularity information across media types

PurposeWith 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/approachThis 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.FindingsThe 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/valueThe 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.

Toward a Greater Understanding of the Use of Nonverbal Cues To Deception in Computer-Mediated Communication

Background: Computer-mediated communication (CMC) is an important part of work life. However, this communication can be dishonest, and when people attempt to judge dishonesty, irrespective of the cues available, they tend to rely on a few nonverbal cues that are not the most reliable. Literature review: According to leakage theory, CMC modes differ from each other in the number of cues to deception they can transmit, potentially affecting one's ability to detect deception in a given medium. There is considerable research on peoples’ use of nonverbal cues across CMC modes to evaluate deception, but limited understanding of the choices they make and the extent to which their deception judgments are impaired or helped by cues they have access to for different CMC modes. Research questions: 1. To what extent are the nonverbal cues that people say they rely on to detect deception shaped by the medium that they use for communication? 2. What are the effects of nonverbal cue availability on deception detection success? Methodology: We conducted an experiment with 132 veracity judges from New Zealand and Jamaica, who observed interview segments in Spanish and Hindi (languages that they did not understand) to isolate the effects of nonverbal cues. They determined the veracity of each segment and listed the things that guided their judgment. Results/discussion: The results suggest that when certain nonverbal cues are available, such as gaze aversion, these suppress attention to more reliable cues (e.g., voice pitch) when judging deception. Redirecting attention to more reliable cues is therefore important. Unexpectedly, cue choice also varied across language by medium. Conclusions: The findings extend the understanding of people's use of nonverbal cues and the extent to which certain cues distract in the deception judgment. Although people rely on vocalic cues in audio-only media and kinesic cues in video-only media, they tend to rely mostly on, and are distracted by, a few kinesic cues for full audiovisual media, even though vocalic cues are available. We also found that people can successfully detect cues to deception, even when their communication mode is relatively bereft of useful information. However, the availability (or lack) of nonverbal cues was not a factor in deception detection success. To improve detection, deception training that targets reliable cues for different media is needed.

Detecting Deception Using Natural Language Processing and Machine Learning in Datasets on COVID-19 and Climate Change

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.

Deception detection methods incorporating discourse network metrics in synchronous computer-mediated communication

The prevalence of deception in computer-mediated communication and the risk of misjudgement based on deceptive information call for effective detection methods of deception. Extant models for online deception detection rely mainly on verbal behaviours of participants while largely ignoring context. Discourse behaviour analysis, which can better investigate the information in context, has been proved effective for online deception detection; nevertheless, these discourse behaviours have been analysed in isolation without referring to other behaviours in context. To achieve the ultimate goal of effective prediction of deception in synchronous computer-mediated communication, this research exploits temporal networks in uncovering the dynamics of deception behaviours, proposes novel deception detection methods using discourse network metrics as predictive features, and empirically evaluates the performances of deception detection methods incorporating three types of predictive features (non-discourse features, discourse features and discourse network metrics). The results suggest that discourse network features are more effective in detecting deception and incorporating these features with non-discourse and discourse features can significantly improve the performance of deception detection. The findings not only demonstrate the efficacy of structural features in deception detection but also offer both methodological and theoretical contributions to deception detection from the perspective of temporal network.

The role of sex differences in detecting deception in computer-mediated communication in English

<p>While deception seems to be a common approach in interpersonal communication, most examination on interpersonal deception sees the sex of the interlocutor as unconnected with the capability to notice deceptive messages. This research studies the truth and deception detection capability of both male and female receivers when replying to both true and deceptive messages from both male and female speakers. The outcomes indicate that sex may be a significant variable in comprehending the interpersonal detection probabilities of truth and of lies. An interaction of variables including the speakers’ sex, receivers’ sex, and whether the message appears to be truthful or deceptive is created to relate to detection capability.</p>

Communication, compassion, and computers: Adolescents' and adults' evaluations of online and face-to-face deception

We explored Canadian adolescents', emergent adults', and adults' understandings of deception in computer mediated communication (CMC) compared to face to face (FtF). Participants between 13 and 50 years read vignettes of different types of questionable behaviour that occurred online or in real life, and were asked to judge whether deception was involved, and the acceptability of the behaviour. Age groups evaluated deception similarly; however, adolescents held slightly different views from adults about what constitutes deception, suggesting that the understanding of deception continues to develop into adulthood. Furthermore, CMC behaviour was rated as more deceptive than FtF in general, and participants scoring higher on compassion perceived vignettes to be more deceptive. This study is a step towards better understanding the relationships between perceptions of deception across adolescence into adulthood, mode of communication, and compassion, and may have implications for how adults communicate with youth about deception in CMC and FtF contexts.

Untangling a Web of Lies: Exploring Automated Detection of Deception in Computer-Mediated Communication

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.

Unweaving a Tangled Web: Exploring Automated Detection of Deception Cues in Online Claims within B2B Incentive Programs

Safeguarding organizations against opportunism and severe deception in computer-mediated communication (CMC) presents a major challenge to CIOs and IT managers. New insights into linguistic cues of deception derive from the speech acts innate to CMC. Applying automated text analysis to archival email exchanges in a CMC system as part of a channel incentive 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 over structure 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.

Social structural behavior of deception in computer-mediated communication

Deception essentially takes place in social interaction. While deception has been studied from the perspective of interpersonal interaction, little is known about social structural characteristics of deceptive communication. To fill the knowledge gap, this research investigates deception behavior in computer mediated communication (CMC) via the lens of social structure by answering the questions of how one deceiver socially interacts with multiple receivers and what structural characteristics can be used to delineate deception in CMC. To this end, we first conceptualize deception in terms of social structure by drawing on the interpersonal deception and social network theories. We then propose a model of structural behaviors of deception in CMC that consists of three components: centrality, cohesion, and similarity, followed by an empirical evaluation of the model with real-world data collected from a game website. The findings of this study provide new evidence that deception is a strategic activity where the deceiver juggles between the dual goals of promoting his or her deceptive agenda and avoiding detection.

On deception and deception detection: Content analysis of computer-mediated stated beliefs

Deception in computer-mediated communication is defined as a message knowingly and intentionally transmitted by a sender to foster a false belief or conclusion by the perceiver. Stated beliefs about deception and deceptive messages or incidents are content analyzed in a sample of 324 computer-mediated communications. Relevant stated beliefs are obtained through systematic sampling and querying of the blogosphere based on 80 English words commonly used to describe deceptive incidents. Deception is conceptualized broader than lying and includes a variety of deceptive strategies: falsification, concealment (omitting material facts) and equivocation (dodging or skirting issues). The stated beliefs are argued to be valuable toward the creation of a unified multi-faceted ontology of deception, stratified along several classificatory facets such as (1) contextual domain (e.g., personal relations, politics, finances & insurance), (2) deception content (e.g., events, time, place, abstract notions), (3) message format (e.g., a complaint: they lied to us, a victim story: I was lied to or tricked, or a direct accusation: you're lying), and (4) deception variety, each tied to particular verbal cues (e.g., misinforming, scheming, misrepresenting, or cheating). The paper positions automated deception detection within the field of library and information science (LIS), as a feasible natural language processing (NLP) task. Key findings and important constructs in deception research from interpersonal communication, psychology, criminology, and language technology studies are synthesized into an overview. Deception research is juxtaposed to several benevolent constructs in LIS research: trust, credibility, certainty, and authority.

On Lying and Being Lied To: A Linguistic Analysis of Deception in Computer-Mediated Communication

This study investigated changes in both the liar's and the conversational partner's linguistic style across truthful and deceptive dyadic communication in a synchronous text-based setting. An analysis of 242 transcripts revealed that liars produced more words, more sense-based words (e.g., seeing, touching), and used fewer self-oriented but more other-oriented pronouns when lying than when telling the truth. In addition, motivated liars avoided causal terms when lying, whereas unmotivated liars tended to increase their use of negations. Conversational partners also changed their behavior during deceptive conversations, despite being blind to the deception manipulation. Partners asked more questions with shorter sentences when they were being deceived, and matched the liar's linguistic style along several dimensions. The linguistic patterns in both the liar and the partner's language use were not related to deception detection, suggesting that partners were unable to use this linguistic information to improve their deception detection accuracy.

Typing or messaging? Modality effect on deception detection in computer-mediated communication

Motivated by the practical significance of deception detection to both organizations and individuals and the recent surge of research interests in deception in computer-mediated communication (CMC), this study examines whether and how the modality of communication media can influence deception detection in CMC. The results of empirical evaluations revealed that the messaging and the chatting modalities were more conducive to deception detection than the typing modality, and the chatting modality is the most preferred for the process of deception detection. Additionally, the effect of message veracity was also investigated. The findings raise broader issues about media properties for deception detection.

Deception in Computer-Mediated Communication

Computer-mediated communication on distance learning brings various effects on thelecturers and the higher-education students. Distance learning has been performed on campusesin Indonesia since the pandemic of Covid-19. This research aims to describe the students’deception via computer-mediated communication. Interpersonal deception and computer-mediated communication theories are chosen to analyze this deception phenomenon. Data were collected through questionnaires and distributed to students in Jakarta as the red zone of the pandemic ofCovid-19. The findings show the students tended to manipulate information before they deliver it tothe audience and to hide their identity or presence during the virtual classroom.

Deception in Computer-Mediated Communication

An integrated model of interpersonal deception, focusing on the particular characteristics and impacts of computer-mediated modalities, is derived and presented. Although there is a wealth of research investigating both mediated communication and deception, there has been relatively little empirical work at the intersection. Our purpose is to provide a model that spans these areas, to summarize relevant literature, and to advance a set of empirically testable propositions to guide future work.

Language dominance in interpersonal deception in computer-mediated communication

Dominance is not only a complicated social phenomenon that involves interpersonal dynamics, but also an effective strategy used in various applications such as deception detection, negotiation, and online community. The extensive literature on dominance has primarily focused on the personality traits and socio-biological influence, as well as various nonverbal and paralinguistic behaviors associated with dominance. Nonetheless, language dominance manifested through dynamically acquired linguistic capability and strategies has not been fully investigated. The exploration of language dominance in the context of deception is even rarer. With the increasing use of computer-mediated communication (CMC) in all aspects of modern life, language dominance in CMC has emerged as an important issue. This study examines language dominance in the context of deception via CMC. The experimental results show that deceivers: (1) demonstrate a different trend of language dominance from truthtellers over time; (2) manipulate the level of language dominance by initiating communication with low dominance and gradually increasing the level over the course of interaction, and (3) display higher levels of dominance in terms of some linguistic behaviors than truthtellers. They suggest that in CMC, deceivers not only adjust the level of language dominance more frequently, but also change it more remarkably than truthtellers.