Queen's University

Dr. David Skillicorn

Professor, School of Computing
LinkedIn profile for Dr. David Skillicorn


My research is focused on building inductive models from data in settings where the interests of modellers and those being modelled are not aligned.  This includes counterterrorism, law enforcement and policing, anti-money-laundering, fraud, and cybersecurity; but also areas that are less obviously adversarial, such as customer relationship modelling.

Two approaches that are especially useful because it is hard for adversaries to disguise their activities and traces are natural language and social networks. I have developed techniques to infer properties such as deception, intentions, personality, emotional state and attidues from language; and also ways to detect the processes that drive abusive language and hate speech, and the use of language for influence. I have also developed techniques to understand the structure of social networks, especially when the edges have different meanings (friends vs colleagues, friends vs enemies) and change with time.

I have authored more than 150 papers, and several books, my most recent one being "Social Networks with Rich Edge Semantics" (with Quan Zheng, Taylor & Francis, 2017). Previous books include: "Understanding High-Dimensional Spaces" (Springer), and "Knowledge Discovery for Counterterrorism and Law Enforcement" (Taylor & Francis). 

My recent work includes inferring systemic nets from corpora, removing a major bottleneck to their wider use, and empirical determination of verbal mimicry.

My latest project is based on Measuring Human Emotion in Short Documents to Improve Social Robot and Agent Interactions.

Most Recent Project

Measuring Human Emotion in Short Documents to Improve Social Robot and Agent Interactions

Social robots and agents can interact with people better if they can infer their affective state (emotions). While they cannot yet recognise affective state from tone and body language, they can use the fragments of speech that they (over)hear. We show that emotions – as conventionally framed – are difficult to detect.

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Other Projects

  • Social Networks with Rich Edge Semantics

    Social Networks with Rich Edge Semantics introduces a new mechanism for representing social networks in which pairwise relationships can be drawn from a range of realistic possibilities, including different types of relationships, different strengths in the directions of a pair, positive and negative relationships, and relationships whose intensities change with time.

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  • Knowledge Discovery for Counterterrorism and Law Enforcement

    Most of the research aimed at counterterrorism, fraud detection, or other forensic applications assumes that this is a specialized application domain for mainstream knowledge discovery. Unfortunately, knowledge discovery changes completely when the datasets being used have been manipulated in order to conceal some underlying activity. 

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