Detecting Deceptive Chat-Based Communication Using Typing Behavior and Message Cues

Detecting lies in investigative interviews through the analysis of response latencies and error rates to unexpected questions

In this study, we propose an approach to detect deception during investigative interviews by integrating response latency and error analysis with the unexpected question technique. Sixty participants were assigned to an honest (n = 30) or deceptive group (n = 30). The deceptive group was instructed to memorize the false biographical details of a fictitious identity. Throughout the interviews, participants were presented with a randomized sequence of control, expected, and unexpected open-ended questions about identity. Responses were audio recorded for detailed examination. Our findings indicate that deceptive participants showed markedly longer latencies and higher error rates when answering expected (requiring deception) and unexpected questions (for which premeditated deception was not possible). Longer response latencies were also observed in participants attempting deception when answering control questions (which necessitated truthful answers). Moreover, a within-subject analysis highlighted that responding to unexpected questions significantly impaired individuals' performance compared to answering control and expected questions. Leveraging machine-learning algorithms, our approach attained a classification accuracy of 98% in distinguishing deceptive and honest participants. Additionally, a classification analysis on single response levels was conducted. Our findings underscore the effectiveness of merging response latency metrics and error rates with unexpected questioning as a robust method for identity deception detection in investigative interviews. We also discuss significant implications for enhancing interview strategies.

Opposing Effects of Response Time in Human–Chatbot Interaction

Research has shown that employing social cues (e.g., name, human-like avatar) in chatbot design enhances users’ social presence perceptions and their chatbot usage intentions. However, the picture is less clear for the social cue of chatbot response time. While some researchers argue that instant responses make chatbots appear unhuman-like, others suggest that delayed responses are perceived less positively. Drawing on social response theory and expectancy violations theory, this study investigates whether users’ prior experience with chatbots clarifies the inconsistencies in the literature. In a lab experiment (N = 202), participants interacted with a chatbot that responded either instantly or with a delay. The results reveal that a delayed response time has opposing effects on social presence and usage intentions and shed light on the differences between novice users and experienced users – that is, those who have not interacted with a chatbot before vs. those who have. This study contributes to information systems literature by identifying prior experience as a key moderating factor that shapes users’ social responses to chatbots and by reconciling inconsistencies in the literature regarding the role of chatbot response time. For practitioners, this study points out a drawback of the widely adopted “one-design-fits-all” approach to chatbot design.

Covert lie detection using keyboard dynamics

Identifying the true identity of a subject in the absence of external verification criteria (documents, DNA, fingerprints, etc.) is an unresolved issue. Here, we report an experiment on the verification of fake identities, identified by means of their specific keystroke dynamics as analysed in their written response using a computer keyboard. Results indicate that keystroke analysis can distinguish liars from truth tellers with a high degree of accuracy - around 95% - thanks to the use of unexpected questions that efficiently facilitate the emergence of deception clues.

An Investigation on the Detectability of Deceptive Intent about Flying through Verbal Deception Detection

Background:

Academic research on deception detection has largely focused on the detection of past events. For many applied purposes, however, the detection of false reports about someone’s intention merits attention. Based on the verbal deception detection paradigm, we explored whether true statements on intentions were more detailed and more specific than false statements on intentions, particularly when instructed to be as specific as possible.

Method:

Participants (n = 222) lied or told the truth about their upcoming travel plans either providing ‘as much information as possible’ (standard instructions) or being ‘as specific as possible’ (i.e., mentioning times, locations, places; specific instructions), resulting in four conditions (truthful vs. deceptive intention by standard vs. specific instructions). We collected data via a custom-made web app and performed automated verbal content analysis of participants’ written answers.

Findings:

We did not find a significant difference in the specificity of participants’ statements. The instruction to be as specific as possible promoted more specific information but did not help to discern honest from deceptive flying intentions.

Conclusion:

The experiment reported here attempted to demonstrate automated verbal deception detection of intentions. The difficulty in capturing genuine intentions, and the non-intrusive, non-interactive questioning approach might explain the null findings and raise questions for further research. We conclude with suggestions for a novel framework on semi-interactive information elicitation.

The “Mail-Order-Bride” (MOB) Phenomenon in the Cyberworld

Information technology (IT) is often an enabler in bringing people together. In the context of this study, IT helps connect matchmaking service providers with those looking for love, particularly when a male seeks to meet and possibly marry a female from another country: a process which results in over 16,500 such ‘mail-order-bride’ (MOB) marriages a year in the United States alone. Past research in business disciplines has been largely silent about the way in which this process unfolds, the perspectives of the participants at different points of time, and the role of IT underlying the MOB matchmaking service. Adopting an interpretivist stance, and utilizing some of the methodological guidelines associated with the Grounded Theory Methodology (GTM), we develop a process model which highlights: a) the key states of the process through which the relationship between the MOB seeker (the man) and the MOB (the woman) unfolds, b) the transitions between states, and c) the triggering conditions for the transitions from one state to another. This study also highlights key motivations of the individuals participating in the MOB process, the effect of power and the role it plays in the dynamics of the relationships, the status of women and how their status evolves during the MOB process, and the unique affordance provided by IT as the relationships evolve.