Towards Extracting Personality Trait Data

from Interaction Behaviour

Nick Fine and Willem-Paul Brinkman

School of Information Systems, Computing and MathematicsBrunel University

{nick.fine, willem.brinkman}@brunel.ac.uk

Keywords: logging, log file recording, user interface skins, reskinning, user interface design

Problem 1: Avoiding AverageAverage user interfaces = average interaction

Why interact with a UI that is designed for theaverage individual?

UI skinning technology allows for easy changeof the UI – but how can this best be achieved?

Problem 2: Segmenting Large User

PopulationsIf not designing for average, need to

targetcertain subsets of the larger

population:– how are they identified?– how are they designed for?

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

Towards the Individual: Designing for Subsets

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)

Experimental Platform: Infrastructure

Internet

ProSkin server

INTERACTIONLOG

ProSkin clientapplication (WebRadio)

QUESTION’REANSWERS

Database

NEWQUESTION’RS

SKINS

INTERACTIONLOGS

USER PROFILES

QUESTIONAIRES

SKINS

- database- listener app- web server

Internet RadioStations

Firewall

100mbps

Hosting Facility, Docklands

Client-Server over TCP/IPMicrosoft .NET 1.1 FrameworkAccess Database

Experimental Platform: Application Architecture

Analysis

Looking for relationships between userPersonality and recorded interactivebehaviour

Personality Dimensions “Big Five” (Costa and McRae)

Openness to new experienceConscientiousnessExtroversionAgreeablenessNeuroticism

(as measured by the IPIP-NEO)

Interactive Behaviours

Number of events in session (N, M, SD)Total events of all sessionsCorrelation – events and N sessionsInterceptSlope

Results

Ethical Issues

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?

Issues

What kind of information is acceptable to record?

• Personally/non personally identifiable?

General demographics e.g. Age, Gender, Country

Content measurese.g. WWW sites visited

Non-Content measurese.g. User interface skin choices/configuration

Personal measures e.g. Personality, Intelligence, Cognitive Style

Session measurese.g. application usage, feature usage, mouse clicks, number of sessions, mean times

Issues

Is it acceptable/possible to record datawhich can then be used to identify theindividual?

If the data recorded is not personallyidentifiable, what potential harm is

there? If no harm, then why the need to

disclose?!

Logging is Already Ubiquitous!Log file recording is andhas been recordinguser interactive behaviourfor 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

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

Giving to Get

How can we gain trust and overcome userscepticism regarding LFR?

If users perceived usability data derivedfrom LFR as harmless then more peoplewould contribute usability data freely.

SETI@home, folding@home, distributed.net

ProSkin?

Questions?