mit 8 presentation
TRANSCRIPT
Some Biases in DH + Data Visualization
Michael Witmore, ”Text: A Massively Addressable Object”
Mills Kelly, “Visualizing Millions of Words”
Franco Moretti, Graphs, Maps, and Trees
Common Trends in DH:
•Focus on big data – questions of scale, distant reading
•Focus on the data set, “the text itself” => seen to have unproblematic access to the real.
•Focus on textual media.
Data Visualization + Reader Response Theory
Informed by reader-response theory: the player is the text.
• How can we use data viz to understand an individual user’s trajectory through a text?
• How can we see each trajectory as itself multiplicitous, internally complex?
•How can we compare trajectories to arrive at different typologies of users, distinct modes of engagement?
•Finally, how can we use data visualization to ask these questions in a medium specific way?
Case Studies: Understanding readers/viewers/players
• Annotation Studio (text)
• Movie Tagger (video)
• Software Studies Initiatives (games)
Understanding Readers – Annotation Studio
Annotation Studio:
•Collaborative Notetaking
•Color-Coding Tags
•Visualizing interaction hotspots and trajectories
Understanding Readers
Understanding Readers
Understanding Readers
Understanding Readers
Understanding Readers
Understanding Viewers – MovieTagger
MovieTagger
•Combining “close reading” with time-based tagging.
•Comparing “expert close readings” from two very different film scholars.
Understanding Viewers
Understanding Viewers
Understanding Viewers
Games + Data Viz
Data Visualization in Games
•To a certain extent, games are unique in that visualizations are already an integral part of gameplay.
•Data visualization as a means to self-knowledge, epistiphillia, navigating complex systems.
•Alex Galloway, Gaming: Essays on Algorithmic Culture – the heads-up display and the non-diegetic.
•Industry research and player tracking; user created maps.
Special problems and potentials of visualizing interaction in games:
• How do we track emergence?
Understanding Players – Software Studies Initiative
William Huber, Fatal Frame II
•Method: Capture videos of gameplay, translate this footage into sequential still frames.
•Visualizing “rhythms and tempos of gameplay.”
•“Quantification of the subjective experience of gameplay”
Understanding Players
William Huber, Fatal Frame II
•Visualizing the traversal of three distinct players (casual vs. hardcore)
•White flash: monsters are killed during that frame.
•Different modes of play: cinematic, navigational, camera, combat.
Understanding Players
William Huber, Fatal Frame II
•Managing the aesthetic experience of flow.
Understanding Players
Noah Wardrip-Fruin, Knights of the Old Republic
•Like MovieTagger, still a matter of linear, temporal flow.
•Limitations of this approach?
Understanding Players - Speculations
Heatmap of Manhattan:
•Tourists vs. locals
•Polarization in the use of space.
Understanding Players
Player Typologies + Modes of Play (Huber)
Types of players (Brown and Vaughn cited in Medler and Magerko):
• Collector
• Competitor
• Director
• Explorer
• Joker
• Kinesthete
• Storyteller
Reading the Reader
The uses of visualization (existing and potential)
• Marketing
• Self-Knowledge
• Competition – data visualization as “score board” or “trophy room”
• Humanistic Research
New approaches, old questions
• What are the different modes of engagement facilitated by a game? Are these modes discrete or overlapping? Are some more likely than others?
• How might the player perform a resistant reading?
• How do such visualizations make the possibility space of the game more transparent?
