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Towards Extracting Personality Trait Data from Interaction Behaviour

Towards Extracting Personality Trait Data from Interaction Behaviour. Nick Fine and Willem-Paul Brinkman School of Information Systems, Computing and Mathematics Brunel University {nick.fine, willem.brinkman}@brunel.ac.uk.

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Towards Extracting Personality Trait Data from Interaction Behaviour

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  1. Towards Extracting Personality Trait Data from Interaction Behaviour Nick Fine and Willem-Paul Brinkman School of Information Systems, Computing and Mathematics Brunel University {nick.fine, willem.brinkman}@brunel.ac.uk Keywords: logging, log file recording, user interface skins, reskinning, user interface design

  2. Problem 1: Avoiding Average Average user interfaces = average interaction Why interact with a UI that is designed for the average individual? UI skinning technology allows for easy change of the UI – but how can this best be achieved?

  3. Problem 2: Segmenting Large User Populations If not designing for average, need to target certain subsets of the larger population: • how are they identified? • how are they designed for?

  4. Approach 1) Segment large user populations by a defining trait – Personality • why Personality? CASA (Reeves and Nass), Similarity Attaction Hypothesis (Byrne and Nelson), Colour Theory 2) Determine Personality through log file recording • Informs designers of the Personality types of the target population without needing to ask the users directly 3) Produce Profiled User Interface Skins (ProSkins) that are designed for the target segment • e.g. Red UI skin colour for extroverts • e.g. Low edge complexity for introverts • e.g. Agreeable personality represented for Agreeable users

  5. Towards the Individual: Designing for Subsets

  6. Log File Recording Segment profiles established using log file recording methods to capture: • User interactive behavioural measures • Mouse clicks (navigation, feature use, sessions) • Effort values • UI Skin selection • User questionnaire data • Personality (IPIP-NEO, TIPI) • UI Skin Preference • Music Preference (STOMP) • General Demographics (age, gender, country)

  7. Experimental Platform: Infrastructure Client-Server over TCP/IP Microsoft .NET 1.1 Framework Access Database

  8. Experimental Platform: Application Architecture

  9. Analysis Looking for relationships between user Personality and recorded interactive behaviour Personality Dimensions “Big Five” (Costa and McRae) Openness to new experience Conscientiousness Extroversion Agreeableness Neuroticism (as measured by the IPIP-NEO) Interactive Behaviours Number of events in session (N, M, SD) Total events of all sessions Correlation – events and N sessions Intercept Slope

  10. Results

  11. Ethical Issues

  12. Position Statement In order to provide personalisation and customisation services greater information about users is required. Log File Recording (LFR) provides a means to collect this information in an unobtrusive manner. How can HCI develop LFR as a research method within an ethical framework?

  13. Issues What kind of information is acceptable to record? • Personally/non personally identifiable?

  14. General demographics e.g. Age, Gender, Country Content measures e.g. WWW sites visited Non-Content measures e.g. User interface skin choices/configuration Personal measures e.g. Personality, Intelligence, Cognitive Style Session measures e.g. application usage, feature usage, mouse clicks, number of sessions, mean times

  15. Issues Is it acceptable/possible to record data which can then be used to identify the individual? If the data recorded is not personally identifiable, what potential harm is there? If no harm, then why the need to disclose?!

  16. Logging is Already Ubiquitous! Log file recording is and has been recording user interactive behaviour for decades: e.g. • web server logs • cookies • media player content • search engines/indexes • any IP access • door security systems • photocopiers • content management systems • license plate recognition systems • CCTV

  17. Protecting Users • Anonymity • Not personally identifiable, therefore no risk to individual privacy • Informed consent • Full disclosure and permission • Ability to view logged data and/or source code • The “open source” philosophy • Ability for user to turn off logging/opt out • User has option to withdraw from logging at any time

  18. Giving to Get How can we gain trust and overcome user scepticism regarding LFR? If users perceived usability data derived from LFR as harmless then more people would contribute usability data freely. SETI@home, folding@home, distributed.net ProSkin?

  19. Questions?

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