A Computational Model of Deception Detection in SMS Scams

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This project will develop a model that integrates many aspects of thought in order to explain why people tend to believe that SMS scams are authentic and, in turn, develop a theory-based intervention to reduce the tendency to overbelieve. Because there are many different types of information that are used when making these decisions, and because of the many aspects of thought that come into play, exploring the process by experiments alone would be costly, time-consuming, and limited in terms of determining the mental representations/processes involved. To date, no theory of deception detection has integrated the many aspects of cognition such as attention, goal-planning, and long term memory, all of which are required to decide whether an SMS message is genuine or deceptive. By relying on a well-established and tested theory, the computational model can explore these issues in a cost-effective and relatively time-efficient manner. This project is innovative insofar as it will be the first domain-general model of deception detection.Work package 1 will develop a domain-general computational model in the context of SMS deception detection. The ACT-R cognitive architecture has been extensively tested and developed for over 40 years. It is a theory of the key structures and processes of the human mind that integrates the many aspects of cognition. This project will use the ACT-R framework to implement a computational model of deception detection. The model will be explored to test how people act when (i) their attention is drawn to factors such as an urgent response being required, (ii) the content of the message and the general suspiciousness of the individual are varied, and (iii) an intervention is implemented, such as giving the option to delay responding to an SMS. These factors will be tested with the ACT-R framework in work package 1.Work package 2 will explore the ACT-R model with experimental testing. These studies will mix laboratory control with real-world application. Participants will interact with a mobile phone while their eye movements are recorded. Eye movement data provides precise timing on what information they are drawn to in the SMS scam and the decision of whether reply or delete the text. This precise timing information allows the project to test the predictions of the ACT-R deception detection model.Work package 3 will develop an intervention based on the results of work packages 1 and 2. Consulting with North Yorkshire police and an SMS scam-prevention organisation (The AntiSocial Engineer Ltd.), and in collaboration with a computer scientist, a cell phone-based application will be developed. Although the exact nature of the application will depend on the findings of the first two work packages, based on the current theories in deception detection it is anticipated that the application may scan for key words, encourage users to verify information by searching online, or allow users to press an 'unsure' button, which will hide the SMS message until they can give time to checking the authenticity of the text.This project aligns with the ESRC research priorities of 'ways of being in a digital age' and 'productivity'. Police forces have limited resources to investigate personal SMS scams, while scam-prevention organisations are noting that SMS attacks are on the rise. The outcome of this project - a smartphone application - aims to encourage users to consider the possibility of a scam and to act with a 'healthy scepticism' in the digital world. A reduced risk of engaging with scams will lessen the burden on police forces and aid scam-prevention organisations in delivering evidence-based interventions to potential victims.We will also work with a local child safeguarding board to translate the model into understanding the tendency to overbelieve offenders in the context of social work, where deception is estimated to be involved in 75% of cases in England & Wales where a child is killed.