our simulation is based on chris starnes. original work by reynolds [8] on the simulation of flocks...
TRANSCRIPT
Our simulation is based on Chris Starnes. original work by Reynolds [8] on the simulation of flocks of birds (or ‘Boids‘) in a manner not subject to the apparent combinatorial explosion of such calculations. We are extending the concept of flocking or clustering to be based on the data relationships (determined by textual comparison) between data points (our ‘Boids‘) rather than purely on adjacency of the Boids. We feel this simulation will be both intuitive and informative, as well as allowing for rich user interaction as Boids can be manually relocated (‘dragged around‘) by the user, and the simulation will react accordingly. Miguel Borromeo. Flock will take advantage of advanced computer graphics hardware and software. We will be using OpenGL to perform the rendering, and making use of OpenCL for parallelized Patrick Webster computation of data clustering and flocking. The implementation will consist of four core components: a storage and analysis module, the graphics engine, the flocking engine, and the user interface. The storage and analysis module defines data sets and storage facilities to easily find and represent facets about the information. The graphics engine will be tasked with rendering and computing the physical Nathan Clark. calculations necessary for our interactive environment. The flocking engine (with the aid of the clustering engine, part of the S&A module) will link Boids together to create ―flocks‖ of related data sets. We will explore what we call ‘implicit social networks,‘ in an effort to improve understanding of the interactions and relationships these networks present. While ‘explicit‘ networks are based on ‘friend lists,‘ bibliographies, and other such explicitly denoted user-user relationships, implicit networks are derived from the ‘activity networks‘ [13] of those users. Research has shown that full comprehension of a social network requires understanding these implicit links, as the explicit links rarely Luke Hersman. hold any correspondence to the actual strength of a relationship [3]. By mapping Twitter as an implicit social network, we will identify what aspects of a network correspond with relationships between Justin Kern. users, and be able to extend that knowledge to identifying corresponding relationships between topics, groups of users, and even individual tweets.
Miguel Borromeo
Chris Starnes
Nathan Clark
Luke Hersman
Justin Kern
Patrick Webster
Video
The problem is that this information is not easily apparent
Our goal was to create a way to easily interpret this information
Twitter was a prime choice for our project
A wealth of information can be gleaned from social web sites.
TwitterFlock
Problem
Design
MeritImplications
Impact
Problem
Initial Focus
Focused initially on technical aspects
Laid out component interactions in advance
Local store
Relationship engine
Flocking engine
GUI
Remote database
Application Components:
TwitterFlock
Problem
Design
MeritImplications
Impact
Evaluation
TwitterFlock
Problem
Design
MeritImplications
Impact
Flocking behavior is unclear
Which word influenced the flocking?
Interaction is limited and confusing
People just want to read the tweets
Evolution
Flocking behavior is unclear
Moved to a yes/no decision
Slowed down the simulation
Which word influenced the flocking?
Added glow lines between flocking tweets
Interaction is limited and confusing
Added tweet text box
Calculated and displayed the most meaningful word
People just want to read the tweets
TwitterFlock
Problem
Design
MeritImplications
Impact Added visual feedback when selecting a tweet
Added the ability to drag tweets
To See Profoundly
“There are some universal cognitive tasks that are deep and profound—indeed, so deep and profound that it is worthwhile to understand them in order to design our displays in accord with those tasks.” -Edward Tufte
Three levels of semantic metaphor
Depth rather than breadth of interaction
Simplicity vs. completeness
TwitterFlock
Problem
Design
MeritImplications
Impact
Sensemaking and Social Networks
Implications
How do we visualize data?
TwitterFlock
Problem
Design
MeritImplications
Impact
Numbers?
Graphs?
Charts?
Text?
This is easy, but what about relationships, semantics, and dynamic nature?
Implications
TwitterFlock
Problem
Design
MeritImplications
Impact
Used bird-based behavior to visualize Twitter content
Twitter has an inherent bird-theme
“Tweeting”, “following”, etc
Why not flocking?
Implications
TwitterFlock
Problem
Design
MeritImplications
Impact
Not limited to flocking:
What we’ve discovered:
Mapping behavior and content conveys the dynamic aspect of data well
Transcends making sense of numerically-based visualizations
Gravitation
Swarming
Broader Impact
Lack of tools to interpret data.
Meaning can be hidden through implicit connections.
Encourages the exploration of social networks.
May make it possible to create a more complete understanding of social networks and their interactions.
Easily expanded to any text-based data.
TwitterFlock
Problem
Design
MeritImplications
Impact
Questions?
TwitterFlock
Problem
Design
MeritImplications
Impact?