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David Nicholas, Ciber: Audience Analysis and Modelling, the case of CIBER and Deep Log Analysis

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Audience analysis and modelling: the case of CIBER and Deep Log Analysis David Nicholas CIBER University College London [email protected] www.ucl.ac.uk/slais/research/ciber
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Page 1: David Nicholas, Ciber: Audience Analysis and Modelling, the case of CIBER and Deep Log Analysis

Audience analysis and modelling: the case of

CIBER and Deep Log Analysis

David NicholasCIBER

University College [email protected]

www.ucl.ac.uk/slais/research/ciber

Page 2: David Nicholas, Ciber: Audience Analysis and Modelling, the case of CIBER and Deep Log Analysis

Background

• CIBER has been working in the media, health, publishing and education fields to help them understand what has happened (and happening) as the result of the digital transition

• CIBER has done this by portraying, in detail, what goes on in the anonymous and volatile virtual environment

• CIBER has done this through providing robust and undisputed evidence of what people do in the virtual environment

Page 3: David Nicholas, Ciber: Audience Analysis and Modelling, the case of CIBER and Deep Log Analysis

CIBER’s methodology: DLA

• Turn digital activity or ‘noise’ into powerful evidence-based audience (consumer) information

• Digital noise (as identified in logs) turned into information behaviour which then enables identification of diversity and establishment of best practice and crude levels of satisfaction. In conjunction with demographic/user information (subscriber or survey) usage data transformed into user data. Then – and only then - can identify real satisfaction, impacts and outcomes (The Holy Grail).

Page 4: David Nicholas, Ciber: Audience Analysis and Modelling, the case of CIBER and Deep Log Analysis

It is absolutely essential

• Findings drive system change - hand and glover with user dynamics. No digital concrete. DLA part of system design

• In a disintermediated environment, where information & content is ubiquitous and users have complete choice digital services will not stay the course unless kept in constant touch with the user.

• Digital visibility a key concept

Page 5: David Nicholas, Ciber: Audience Analysis and Modelling, the case of CIBER and Deep Log Analysis

Particular importance of outcome/impact data

• Access card has run its course• Have to move beyond that

warm feeling• What is poor or productive and

profitable information behaviour?

• How do we know 24/7 provision is helping us?

• Are there obvious outcomes associated with it?

• The car park question!

Page 6: David Nicholas, Ciber: Audience Analysis and Modelling, the case of CIBER and Deep Log Analysis

Benefits of DLA

• Size and reach. Massive numbers, no need to take a sample• Direct & immediately available record of what people have

done: not what they say they might, or would, do; not what they were prompted to say, not what they thought they did. Perceptions of time in surveys very subjective and lags behind the data and people leave their memories behind them in cyberspace.

• Data are unfiltered and provide a reality check sometimes missing from questionnaire and focus group

• Data real-time and continuous Creates a fantastic digital lab environment for the monitoring of change

• Raises the questions that need to be asked by questionnaire and interview.

• Enables level-playing field platform comparisons

Page 7: David Nicholas, Ciber: Audience Analysis and Modelling, the case of CIBER and Deep Log Analysis

The digital information footprintThe digital information footprint

Information Seeking Characteristics

ActivityMetrics

User Characteristics

1. Number of pages viewed2. Number of full-text downloads3. Number of sessions conducted4. Site penetration5. Time spent viewing a page6. Time spent on a session7. Number of searches undertaken in session8. Number of repeat visits made9. Number of sources used10. Number of views per source

1. Subject/ discipline2. Job status3. Geographical location4. Name of organisation5. Type of organization used to access the service6. User demographics: gender, age etc (if available)

A. Type of content viewed1. Number of sources used in a session2. Names of sources used/not used3. Subject of source5. Age of source used6. Type of material viewed 7. Type of full-text view8. Size of source used9. Publication status of article

B. Searching style1.Search approach adopted2. Number of searches conducted in a session3. Number of search terms used in search4. Form of navigation5. From where users arrive from

29 Key Features

Page 8: David Nicholas, Ciber: Audience Analysis and Modelling, the case of CIBER and Deep Log Analysis

Snippets from CIBER research

• Evaluating the usage and impact of e-journals in the UK. RIN, 2008-

UK National E-Books Observatory. JISC, 2008-2009

Digital Lives, (with British Library). AHRC, 2007-2009

• Behaviour of the Researcher of the Future (Google Generation). British Library and JISC, 2007

Authors as users: a deep log analysis of ScienceDirect. Elsevier; 2005-2006;

Maximizing Library Investments in Digital Collections Through Better Data Gathering and Analysis. US Institute of Museum and Library Services; 2005-2007;

The digital health consumers of BBC. Department of Health; 2004-2005;

E Learning and the World Wide Web – accessibility and participation for people with cognitive disabilities. ESRC, 2004-2006;

The Web, the kiosk, digital TV and the changing and evolving face of consumer health information provision: a national impact study. Department of Health; 2000 – 2004;

An evaluation of pilot projects exploring the health applications of digital interactive television. Department of Health; 2001–2002.

Page 9: David Nicholas, Ciber: Audience Analysis and Modelling, the case of CIBER and Deep Log Analysis

Robots!

• Best kept secret• Around half of all visitors

to a scholarly site are robots

• In case of some AHRC funded sites account for 90% of visitors

• Makes you realize how things have changed

Page 10: David Nicholas, Ciber: Audience Analysis and Modelling, the case of CIBER and Deep Log Analysis

Navigators

• Navigating towards content in very large digital spaces a major human activity.

• People spend half their time viewing content, rest of the time they are trying to find there way to it (or out of it).

• So many possible routes to content people get lost (excited)

Page 11: David Nicholas, Ciber: Audience Analysis and Modelling, the case of CIBER and Deep Log Analysis

Diversity

• National differences: Germans the most ‘successful’ searchers and most active information seekers. Canadians and Australians more interested in older content

• Age differences: older users more likely to come back, and view abstracts. Elderly users had most problems searching – two thirds of searches obtained zero returns!

• Gender differences: women more likely to view articles in HTML and return to a site (less promiscuous!)

Page 12: David Nicholas, Ciber: Audience Analysis and Modelling, the case of CIBER and Deep Log Analysis

Brand, don’t go there, there are big problems

• Difficult in cyberspace: responsibility/authority almost impossible in a digital environment – so many players

• Also what you think is brand is not what other people think. TESCO!

• And then there is cool. Facebook!

Page 13: David Nicholas, Ciber: Audience Analysis and Modelling, the case of CIBER and Deep Log Analysis

Characteristic information behaviour in the virtual space

• In broad terms scholarly behaviour can be portrayed as being active , bouncing, navigating, checking and viewing. It is also promiscuous, diverse and volatile

• Does this constitute a dumbing down?

Page 14: David Nicholas, Ciber: Audience Analysis and Modelling, the case of CIBER and Deep Log Analysis

Dumbed down information seeking?

• Study confirms what many are beginning to suspect: that the web is having a profound impact on how we conceptualise, seek, evaluate and use information. What Marshall McLuhan called 'the Gutenberg galaxy' - that universe of linear exposition, quiet contemplation, disciplined reading and study - is imploding, and we don't know if what will replace it will be better or worse. But at least you can find the Wikipedia entry for 'Gutenberg galaxy' in 0.34 seconds

Page 15: David Nicholas, Ciber: Audience Analysis and Modelling, the case of CIBER and Deep Log Analysis

Think big, think observatory: JISC National E-Book Observatory

• A geographical plot of IP addresses of participating universities

• Survey ran between 18 Jan and 1 March 2008, over which period 22,437 responses were received from 120+ universities.

• Logs – November to May. All page views to MyiLibrary titles - 3,600,000. JISC titles: views 337,500; sessions 32,800; separate IPs 8,871

Page 16: David Nicholas, Ciber: Audience Analysis and Modelling, the case of CIBER and Deep Log Analysis

Number of page views to 26 JISC books

4000

3000

2000

1000

0

Page 17: David Nicholas, Ciber: Audience Analysis and Modelling, the case of CIBER and Deep Log Analysis

JISC books – subject comparison (page views)

MAYAPRMARFEBJANDECNOV

100

90

80

70

60

50

40

30

20

10

0

SUBJECT

Media

Engineering

Business

29363428313535

17

2727

3118

1619

54

3639

41

514945

Page 18: David Nicholas, Ciber: Audience Analysis and Modelling, the case of CIBER and Deep Log Analysis

Site penetration (JISC v other books)

BothJisc onlyOther only

100

90

80

70

60

50

40

30

20

10

0

Views in session

over 20

11 to 20

4 to 10

2 to 3

One

502324

27

2423

22

3635

1111

68

Page 19: David Nicholas, Ciber: Audience Analysis and Modelling, the case of CIBER and Deep Log Analysis

Ranked JISC book usageRank JISC E-books ranked by usage Use %

1234567891011121314151617181920212223242526

Media Gender and IdentityIntegrated Marketing Communication Marketing Strategy & Competitive Positioning Management Concepts & PracticesOrganisational Behaviour and Analysis: An Integrated Cinema Studies: The Key Concepts Chemical Engineering Volume 6: Chemical Engineering DPower without Responsibility The Dynamics of Employee Relations Engineering Materials 1Public Relations HandbookStructural and Stress Analysis Media Institutions and Audiences Chemical Engineering Volume 2Engineering Materials 2Aerodynamics for Engineering StudentsMeasurement and Instrumentation Principles Modern Structural Analysis Fundamentals of Wireless Communication English for JournalistsWriting for JournalistsA Short Course in Foundation Engineering A Short Course in Soil and Rock Slope EngineeringBetter Places to LiveConceptual Structural Design Better Places to Work

40490380433427931270300972890221800168381419910372

8989708065446032536951204823418941183872367936152198211119561494

12.011.310.2

9.38.98.66.55.04.23.12.72.11.91.81.61.51.41.21.21.11.11.1

.7

.6

.6

.4

Page 20: David Nicholas, Ciber: Audience Analysis and Modelling, the case of CIBER and Deep Log Analysis

Type of page viewed: all books

72.4%

5.1%

11.4%

8.2%

2.9%

Content

Searches

Menus

Home page

Other

Page 21: David Nicholas, Ciber: Audience Analysis and Modelling, the case of CIBER and Deep Log Analysis

Top ten universities viewing Integrated Marketing Communication

University Percentage of all use

Leeds 7.2

Coventry 6.5

Worcester 5.7

Glamorgan 5.5

Sheffield 5.1

Strathclyde 4.7

St Andrews 4.3

Hertfordshire 3.4

Stirling 2.6

Bournemouth 2.6

Page 22: David Nicholas, Ciber: Audience Analysis and Modelling, the case of CIBER and Deep Log Analysis

Plug for book

•http://www.facetpublishing.co.uk/index.shtml


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