Automating Content Analysis of Video Games

1
Automating Content Analysis of Video Games

• T. Bullen and M. KatchabawDepartment of Computer
  ScienceThe University of Western Ontario
• N. Dyer-WithefordFaculty of Information and
  Media StudiesThe University of Western Ontario

2
Outline

• Introduction
• Automating Content Analysis
• Prototype Implementation
• Experiences and Discussion
• Concluding Remarks

3
Introduction

• Content analyses of video games involve coding,
  enumerating, and statistically analyzing various
  elements and characteristics of games
• This includes violence, offensive language,
  sexual content, gender and racial inclusiveness,
  and so on
• While content analysis has its limitations, it is
  invaluable in providing a quantitative
  assessment of games to go with more qualitative
  analyses
• It can be an important tool to many people
  dealing with various aspects of games and the
  games industry

4
Introduction

• Problems arise, however, when one tries to apply
  traditional content analysis processes, for
  example from film or television, to games
• Processes are manual and are consequently time
  consuming and labour-intensive
• This tends to result in significantly reduced
  play times or limiting analyses to only a very
  few games
• Traditional analyses also tend not to consider
  interactivity and non-linearity that occurs in
  games
• The rapid rate at which games are released and
  the industry evolves makes keeping up difficult

5
Introduction

• In the end, with the limited time and resources
  often available, it is exceedingly difficult to
  perform thorough content analyses on even
  areasonable portion of games
• To address these problems, our current work
  examines automating the process of content
  analysis for video games
• Through automation, it is hoped that time and
  resources can be used more efficiently
  andeffectively to permit more thorough studies

6
Automating Content Analysis

• To automate content analysis, we take advantage
  of the fact that, unlike other forms of media,
  video games are software executing onsome kind
  of computing device
• This can permit two forms of automation
• Partial automation software executing along
  side the game monitors game execution and
  collects and reports the data normally collected
  manually
• Full automation further software elements take
  the role of the player and generate gameplay
  experiences without the need for a human player

7
Automating Content AnalysisInstrumentation
8
Prototype Implementation

• As a proof of concept, we have used our
  instrumentation framework to instrument Epics
  Unreal Engine to enable automated content
  analyses of Unreal-based games
• Unreal is a popular engine amongst professional
  and amateur developers, providing numerous
  possible games for content analysis experiments
• Instrumentation was implemented using the
  UnrealScript language
• Source level access to the engine was not
  available

9
Prototype Implementation
10
Prototype Implementation

• Sensors have been developed to collect a wide
  variety of data useful for content analyses
• Death of characters, weapon use by characters,
  use of offensive language, gender and racial
  diversity in characters, and a variety of other
  game statistics
• Data can be reported throughout a game or only as
  summaries at the end of games
• Sensors can be configured at run-time to tailor
  the data collected to the needs of the content
  analyses being conducted

11
Experiences and Discussion

• To validate our prototype implementation, we
  conducted several content analysis experiments on
  Unreal Tournament 2004
• This game is one of the flagship titles drivenby
  the Unreal Engine
• It is a fairly popular First Person Shooter that
  has numerous gameplay options
• Several different game types and rule sets
• Individual and team-based games
• Single player, multiplayer, and spectator modes

12
Experiences and DiscussionDeathmatch Game

• ------------Level Info------------
• Level Name Rrajigar
• Game Type DeathMatch
• Total Players 14
• AI Players 13
• Human Players 1
• Spectators 0
• Male Players 13
• Female Players 1
• Level Loaded 02645
• Game Finished 03029
• Gameplay Elapsed (Seconds) 240.88
• AI Dialog 28
• Human Dialog 27
• ----------------------------------
• ----------All Player Stats--------
• Total Deaths 47
• Total Suicides 1
• Total Kills 46

• Total Male Deaths 35
• Total Deaths Caused By Females 6
• Total Deaths Caused By Males 41
• ----------------------------------
• --------Local Player Stats--------
• Player Deaths 1
• Player Suicides 0
• Player Killed 1
• Deaths Caused By Player 25
• Player Killed By AI 1
• Player Killed By Human 0
• Player Killed By Male 1
• Player Killed By Female 0
• AI Deaths Caused By Player 25
• Human Deaths Caused By Player 0
• Female Deaths Caused By Player 7
• Male Deaths Caused By Player 18
• Deaths Witnessed By Player 29
• ----------------------------------

13
Experiences and DiscussionOnslaught Game

• ------------Level Info------------
• Level Name Arctic Stronghold
• Game Type Onslaught
• Total Players 12
• AI Players 11
• Human Players 1
• Spectators 0
• Male Players 9
• Female Players 3
• Level Loaded 231643
• Game Finished 23323
• Gameplay Elapsed (Seconds) 964.18
• AI Dialog 216
• Human Dialog 31
• ----------------------------------
• ----------All Player Stats--------
• Total Deaths 142
• Total Suicides 5
• Total Kills 137

• Total Male Deaths 116
• Total Deaths Caused By Females 8
• Total Deaths Caused By Males 134
• ----------------------------------
• --------Local Player Stats--------
• Player Deaths 4
• Player Killed 4
• Deaths Caused By Player 23
• Player Killed By AI 4
• Player Killed By Human 0
• Player Killed By Male 4
• Player Killed By Female 0
• AI Deaths Caused By Player 23
• Human Deaths Caused By Player 0
• Female Deaths Caused By Player 9
• Male Deaths Caused By Player 14
• Deaths Witnessed By Player 43
• ----------------------------------
• ------------Team Info-------------

14
Experiences and Discussion

• Quality of data
• Data collected through automation matched manual
  results, and in some cases was better
• Quantity of data
• We found that we could collect massive amounts of
  data with no visible impact on gameplay, even
  when data was reported throughout a game
• Partial versus fully automated analyses
• We found that results could be very different
• Which is ultimately better?

15
Concluding Remarks

• Content analysis plays several important roles to
  the video games industry, but is unfortunately an
  arduous task to complete in a thorough fashion
• Our current work addresses this issue by
  providing an automated approach to content
  analysis based on software instrumentation
• Initial experimentation with a prototype
  implementation of this approach demonstrates its
  usefulness and shows great promise

16
Concluding Remarks

• Directions for future work include the following
• Conduct further experimentation and more detailed
  content analyses of Unreal Tournament 2004, and
  combine qualitative analyses with our results
• Expand experimentation to other Unreal-based
  games
• Investigate instrumentation of other popular game
  engines and conduct further analyses this way
• Create sensors for measuring other content
  metrics
• Further explore the issue of partial versus fully
  automated content analyses