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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…

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content analysis of interactive media

Content Analysis of Interactive Media

Paul Skalski

Cleveland State University

  • 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).
sidebar the web 2 0 ugm
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!
four considerations
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
1 creating content
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.
specific web 2 0 content issues
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.
specific v g content issues
Specific V.G. Content Issues
  • Smith (2006) identifies the following:
  • 1. Player Skill
    • Depending on skill, players may play in different ways, results in different content
  • 2. Time Frames
    • Whole games cannot be sampled like TV shows or movies.
  • 3. Character Choice
    • Players have increasing control over their characters.
2 searching
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.
3 archiving
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
  • Also software options for video/audio
4 coding
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.
example shelton skalski 06
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
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!)
  • 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.
  • How prevalent is “controversial” content, as of 2006?
study overview
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 “anti-academic” content appear on Facebook, through method of content analysis.
research questions
Research Questions
  • RQ1: How prevalent is controversial content on Facebook?
  • RQ2: How frequent is anti-academic behavior compared to pro-academic behavior?
  • 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.
  • 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
  • Photo variables:
    • 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
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).
selected results content by type profile frequencies percentages
Selected results: Content by type (profile frequencies/percentages)
  • Interests:
    • Alcohol (23/11.1%)
    • Partying (14/6.7%)
    • Profanity (5/2.4%)
    • Drug Use (4/1.9%)
  • Wall Posts:
    • Alcohol (76/36.5%)
    • Partying (48/23.1%)
    • Profanity 41/17.7%)
    • Drug Use (3/1.4%)
selected results content by type profile frequencies percentages22
Selected results: Content by type (profile frequencies/percentages)
  • Photos:
  • 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%)
limitations and future directions
Limitations and Future Directions
  • Limitations:
    • Sample only from University of Minnesota
    • Private profiles much more common now
    • Limited content categories and sources
    • Photo sampling technique
  • Future research:
    • More multivariate analyses
    • Linking content analysis and survey data
the end
The End
  • Questions?
  • Comments?
  • Suggestions?
  • For a copy of the paper and any of the coding materials, contact me, via email ( or Facebook! 