OU Seminar 18 March 2014

Collecting and
analysing text and
images from
Web 2.0
Katrina Pritchard
and
Rebecca Whiting
Research part funded by
Richard Benjamin Trust
(Early Career Award 1103)
@ageatwork
http://ageatwork.wordpress.com © 2014 Katrina Pritchard and Rebecca Whiting. All rights reserved.

@ageatwork http://ageatwork.wordpress.com
Why bother collecting
and analysing texts and
images from Web 2.0?

Big data? Better data?
 More data, meta data, better data
 Synchronous and cotemporaneous data
 Archive and archaeological data
 Big data as the capacity for big research (Boyn and
Crawford, 2012)
 Big data as mapping and mining the Web

Small data in a big data world
 Snippets, fragments and hints at data
 tweets, youtube videos, slideshare presentations, posts
and comments etc.
 data that might never be data (e.g. moderation)
 Composite/transient nature of websites and online
media
 Images, text, tags, hyperlinks combing in “a unique
mixture of the ephemeral and the permanent”
(Schneider & Foot, 2004:115)
 How we navigate the maps and negotiate the
mines
 Socio-material and practice-based perspectives

Relevance to organising
and organisations?
 Unpack and explore what we might previously
have labelled „context‟ or ignored
 Look at interactions between organizations and/or
the ways in which organizations engage with others
via the internet
 Examine the ways in which individuals (including
employees, customers etc.) engage with different
organizations

Relevance to organising
and organisations?
 Web 2.0 “permeates and even replaces traditional
forms of organizing” (Pablo and Hardy, 2012: 822)
 Challenges the “assumption that organising
necessarily occurs in organisations”
(Ashcraft, 2007:11)
 “media spectacle” (Tan, 2011): follow stories as they
unfold across various different media

Our research:
age at work

How are notions of age, age
identities and related
concepts, e.g.
generations, socially constructed
in Web 2.0 media in relation to
issues of work
Data Collection
Data types

Data Collection: Practical Steps
 150 days of data collection (initially)
 Google and Nexis alerts, Twilerts and website
change detection alerts
 Review of alerts and „snowballing‟, usually delayed
by 3-10 days:
 Checking too soon did not allow for hyperlinks and
comments to be posted, wait too long and content
may ‘move’ or be lost
 Following organizations/voices both identified in
advance and via the daily alerts

Data Types: Practical steps
 Around 6 relevant items from google/nexis per day
giving approximately 900 sources which may
include text, images, video items
 Around 50 relevant tweets from twilert per day
giving approximately 7750 tweets
 Text and images cut and paste into word and then
imported into NVivo. Word documents ranged
from 1 to 60 pages in length

[USERNAME] The Older Worker and the Changing Labor Market: New
Challenges for the Workplace: As the country‟s workforce age...
http://t.co/U8s1017s
Thu, 01 Dec 2011 03:44:58 from twitterfeed reply view
[USERNAME] says, say what U want but my older co-worker = the
nicest @ times (he recommended the perfect Belgium waffle sundae
bc I'm sick). It helped!
Thu, 01 Dec 2011 01:27:59 from Tweetings for iPhone reply view
[USERNAME] Tonight, my 24 year old co-worker thought I was younger
than her. Apparently I have a "young face"...yay for 29 year old me ;-)
Thu, 01 Dec 2011 06:07:24 from HootSuite reply view

Research Challenges

Data
management
Data Analysis
Ethical reviews

Data management
 Nvivo was both a blessing and a curse:
 Problems with working across different versions (Version 10 not
available at start of project)
 Format of word proforma created problems
 Capture of alerts by day created docs that had to be broken up
as too big to import into NVivo
 Transcription practicalities of different media
 Various options for sharing/backing up data
became rather complicated
 Copyright issues: need to buy rights to images if
wanted to use them in publication – or do we?

Analysis
 Micro-level analysis:
 Item specific analysis: Text, Image etc
 Combined analysis: Relationship between
items (e.g. Headline, image, text, comments)
 Meso-level analysis:
 Conversations/Themes (including re media)
 Interactions between ‘Voices’ and ‘Topics’
 Timelines around ‘Events’
 Macro-level analysis:
 Relationships between Media, Voices and
Conversations
 Ebbs, flows and influencing around broader
Discourses and genres

Ethical issues
 Major debate within e-research (see Ess, C.
(2009), Digital Media Ethics, Cambridge, Polity Press)
 Key issues relate to:
 Public vs. private spaces on the internet
 BPS (2007, p. 3) whether the research activity may or
may not pose an “additional threat to privacy over
and above those that already exist”
 Authenticity
 “Cloaking” (subtle changes to protect identity)
 Practical issues of contacting participants
 Traceability (ability to Google a quote and identify the
source anyway)


Ethical issues
 We excluded data from private discussion boards
and members only areas, focusing on „public‟ data
sources
 Maintained broad categorisation of key voices and
sites when reporting data (e.g. as news, HR
professional groups, consultancy
organizations, campaign groups or government
agencies) rather than anticipating specific
identification
 Committed to anonymisation of individuals for most
detailed data but this is an ongoing area for review

Emerging ideas:
where are we now

Where are we now?
 Methodologically:
 Defining „digital dilemmas‟
 Examining „real participants‟ consumption off-line
 Age at work:
 Discursive construction of generations (baby boomers
and the lost generation)
 Visual analysis of stock images and gendered ageing
 Interest group campaigning on the Web and the
„missing million‟

Digital dilemmas
Scale
Context
Participant
Ethics
Reflexivity
Quality
Distinctiveness

Examining consumption off
line
 Current focus on „stock‟ images:
 Group photo-elicitation (image out of context, image
in context)
 Plan qualitative photo-response survey subject to
funding
 How to examine interactivity in Web 2.0?
 What aspects of consumption can be captured e.g.
via comments etc.
 How to explore internet practice off and online

OU Seminar 18 March 2014

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OU Seminar 18 March 2014