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Miguel Borromeo

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Miguel Borromeo

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  1. 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 Webstercomputation 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. Chris Starnes Miguel Borromeo Patrick Webster Nathan Clark Luke Hersman Justin Kern

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  3. Problem A wealth of information can be gleaned from social web sites. TwitterFlock Problem Design The problem is that this information is not easily apparent Merit Implications Impact Our goal was to create a way to easily interpret this information Twitter was a prime choice for our project

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

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

  6. Evolution Added the ability to drag tweets Added glow lines between flocking tweets Flocking behavior is unclear Slowed down the simulation Which word influenced the flocking? Moved to a yes/no decision Interaction is limited and confusing Added tweet text box Added visual feedback when selecting a tweet People just want to read the tweets Calculated and displayed the most meaningful word TwitterFlock Problem Design Merit Implications Impact

  7. To See Profoundly Sensemaking and Social Networks TwitterFlock Problem “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 Design Merit Implications Impact Three levels of semantic metaphor Depth rather than breadth of interaction Simplicity vs. completeness

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

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

  10. Implications 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 TwitterFlock Problem Design Merit Implications Impact

  11. 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 Merit Implications Impact

  12. Questions? TwitterFlock Problem ? Design Merit Implications Impact

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