1 / 24

Content Analysis of Interactive Media

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…

iria
Download Presentation

Content Analysis of Interactive Media

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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! 

More Related