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Content Analysis of Interactive Media
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  1. Content Analysis of Interactive Media Paul Skalski Cleveland State University

  2. 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).

  3. 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!

  4. 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

  5. 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.

  6. 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.

  7. 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.

  8. 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.

  9. 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

  10. 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.

  11. 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)

  12. 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!)

  13. Sample Facebook Profile

  14. 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. • How prevalent is “controversial” content, as of 2006?

  15. 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.

  16. Research Questions • RQ1: How prevalent is controversial content on Facebook? • RQ2: How frequent is anti-academic behavior compared to pro-academic behavior?

  17. 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.

  18. 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

  19. Measures • 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

  20. 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).

  21. 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%)

  22. 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%)

  23. 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

  24. 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! 