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

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. Problem Design Merit Implications Impact

Focused initially on technical aspects Laid out component interactions in advance Local store Relationship engine Flocking engine GUI Remote database Application Components: Problem Design Merit Implications Impact

Problem Design Merit Implications Impact Flocking behavior is unclear Which word influenced the flocking? Interaction is limited and confusing People just want to read the tweets

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 Problem Design Merit Implications Impact Added visual feedback when selecting a tweet Added the ability to drag tweets

“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 Problem Design Merit Implications Impact Sensemaking and Social Networks

How do we visualize data? Problem Design Merit Implications Impact Numbers? Graphs? Charts? Text? This is easy, but what about relationships, semantics, and dynamic nature?

Problem Design Merit Implications Impact Used bird-based behavior to visualize Twitter content Twitter has an inherent bird-theme “Tweeting”, “following”, etc Why not flocking?

Problem Design Merit Implications 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

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. Problem Design Merit Implications Impact

Problem Design Merit Implications Impact