Content Analysis of
Interactive Media
Paul Skalski
Cleveland State University
Background
Since the writing and publication of the
Content Analysis Guidebook, there has
been increased interest in interactive
media content, particularly:
 Video games! And…
 Web 2.0 sites or User Generated Media
(UGM).
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Sidebar: The Web 2.0/UGM
Has exploded in popularity in the past 5
years. What are prominent examples?
 Facebook
 MySpace
 YouTube
 Wikipedia
 What do these have in common???
 4 of the 11 MOST visited websites!
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Four Considerations
Key issues for the content analysis of
interactive media include:
 1. Creating content
 2. Searching for content
 3. Archiving content
 4. Coding/analyzing content
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1. Creating Content
The fundamental difference between old
media and newer interactive media.
 Users are in charge of much of what
content looks like, with some restrictions.

Web 2.0 users can create content within the
templates provided by sites.
 Video game players have (some) control
over what happens in a game, affecting the
content.
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Specific Web 2.0 Content Issues
User Generated Media (UGM) vs. User
Collected Media (UCM)—the latter refers
to activities such as posting videos from
TV on YouTube.
 Also: The templates sites provide may
change over time, necessitating FLUID
codebooks to match fluid content.
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Specific V.G. Content Issues
Smith (2006) identifies the following:
 1. Player Skill
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2. Time Frames
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Depending on skill, players may play in
different ways, results in different content
Whole games cannot be sampled like TV
shows or movies.
3. Character Choice
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Players have increasing control over their
characters.
2. Searching
How do you select content for inclusion
in a sample?
 With games, similar procedures can be
used that have been used in TV and
movie content analyses—e.g., selecting
the most successful titles.
 With Web 2.0 sites, there is greater
difficulty due to (potentially) millions of
equal sampling units.
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3. Archiving
How do you store units for analysis?
 With games, typical procedure has been
to record games as players play and
store content on DVD (though DVR
options now)
 With Web 2.0 pages, options include:

Print screen
 Saving the file
 Creating PDFs
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Also software options for video/audio
4. Coding
Analyzing the archived content. Includes:
 1. Identifying units of analysis (e.g.,
individual user posts, game characters)
 2. Creating a codebook
 3. Creating coding sheets (may be
electronic now)
 4. Training, coding, intercoder reliability
assessment, etc.
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Example: Shelton & Skalski (06)
Content analysis of Facebook, created
by Mark Zuckerberg at Harvard in 2004
as online college social network.
 Spread to other universities and now has
300 million unique users, including more
than 90% of college students nationwide
(plus just about everyone else now).
 Survey finding: More than 2/3 of users
log in every day, for average of almost
20 minutes (Vance & Schmitt, 2006)

What’s on Facebook?

Users of Facebook create profiles that
allow them to:
Share personal information.
 Communicate through Wall posts and
private messages.
 Create and join special interest groups.
 Add software applications (“killer app”)
 Post and view photos (number one photo
site!)

Sample Facebook Profile
Controversy!

The CONTENT of Facebook came under
fire early, after searches by university
officials, athletic offices, and employers:
Campus police using site for investigations.
 Top LSU swimmers lost scholarships.
 Illinois University grad denied consulting job
in Chicago based on interests.
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How prevalent is “controversial” content,
as of 2006?
Study Overview
Although media coverage might suggest
Facebook is filled with negative content,
very little empirical evidence exists.
 Present study set out to examine the
extent to which “pro-academic” and “antiacademic” content appear on Facebook,
through method of content analysis.

Research Questions
RQ1: How prevalent is controversial
content on Facebook?
 RQ2: How frequent is anti-academic
behavior compared to pro-academic
behavior?
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Sample
Primary unit of analysis and sampling:
The profile (and corresponding photos).
 QUESTION: What’s the best way to
draw a random sample of Facebook
profiles?
 ANSWER: The site has (had) a built-in
random selector!
 Selected profiles and photo sets
sampled and archived in PDF format.
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Measures
All variables except sex and age coded
as “present” or “absent.”
 Several basic profile content variables.
 Interests/Wall post content variables:

Reference to partying
 Reference to alcohol
 Reference to drug use
 Profanity
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Measures
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Photo variables:
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Partying shown
Alcohol shown
Alcohol consumption shown
Drugs shown
Drug use shown
Physically/sexually suggestive contact
Nudity
Nonverbal aggression
Studying/reading
Meeting with a group
Sitting in class
Training and reliability
Five coders given detailed codebooks
and coding sheets, which were refined
during extensive training.
 Preliminary coding revealed need for two
sets of coders: One profile (3), and one
photos
 Cohen’s kappa on all but two variables
was .80 or above (interests reference to
partying = .66; drug use interest = .71).
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Selected results: Content by type
(profile frequencies/percentages)
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Interests:
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Alcohol (23/11.1%)
Partying (14/6.7%)
Profanity (5/2.4%)
Drug Use (4/1.9%)
Wall Posts:
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Alcohol (76/36.5%)
Partying (48/23.1%)
Profanity 41/17.7%)
Drug Use (3/1.4%)
Selected results: Content by type
(profile frequencies/percentages)
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Photos:
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Alcohol Shown (110/52.9%)
Partying (95/45.7%)
Sexually Suggestive Contact (51/24.5%)
Alcohol Consumption Shown (28/13.5%)
Nonverbal Aggression (9/4.3%)
Drugs Shown (7/3.4%)
Drug Use Shown (4/1.9%)
Studying/Reading (2/1.0%)
Sitting in Class (2/1.0%)
Meeting with a Group (2/1.0%)
Nudity (0/0%)
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Limitations and Future Directions
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Limitations:
Sample only from University of Minnesota
 Private profiles much more common now
 Limited content categories and sources
 Photo sampling technique
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Future research:
More multivariate analyses
 Linking content analysis and survey data
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The End
Questions?
 Comments?
 Suggestions?
 For a copy of the paper and any of the
coding materials, contact me, via email
(p.skalski@csuohio.edu) or Facebook! 
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