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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 l.jpg

Content Analysis of Interactive Media

Paul Skalski

Cleveland State University

Background l.jpg

  • 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 l.jpg
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

    • 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.

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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

  • Also software options for video/audio

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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)

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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!)

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  • 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?

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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.

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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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  • 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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  • 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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  • 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

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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)

  • 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%)

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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%)

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Limitations and Future Directions frequencies/percentages)

  • 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

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The End frequencies/percentages)

  • Questions?

  • Comments?

  • Suggestions?

  • For a copy of the paper and any of the coding materials, contact me, via email ([email protected]) or Facebook! 