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Michael S. Bernstein, Mark S. Ackerman, Ed H. Chi, Robert C. Miller,[object Object]
Ratio of “Understanding Users” papers,[object Object],to “Systems, Tools, Architectures and Infrastructure” papers ,[object Object],submitted to the Interaction Beyond the Individual track at CHI 2011.,[object Object],4:1,[object Object]
Trouble: Exponential Growth,[object Object],Your usage data is not really compelling because only a small fraction of Facebook is using the application. Worse, your numbers aren’t growing in anything like an exponential fashion.,[object Object],– CHI metareviewer, paraphrased,[object Object]
Suggestion: Exponential Growth,[object Object],Separate evaluation of spread from steady-state.,[object Object],Which claim is the paper making?,[object Object]
Trouble: Snowball Sampling,[object Object],The authors’ choice of study method – snowball sampling their system by advertising within their own social network – potentially leads to serious problems with validity.,[object Object],– CHI metareviewer, paraphrased,[object Object]
Suggestion: Snowball Sampling,[object Object],Snowballing is inevitablein social systems. It is fundamental to how they operate.,[object Object]
The Trouble with Social Computing Systems Research
NoveltyBetween a Rock and a Hard Science,[object Object]
sociotechnical,[object Object]
sociotechnical,[object Object],studiersbuilders,[object Object]
sociotechnical,[object Object],studiersbuilders,[object Object],Fatal Flaw Fallacy [Olsen],[object Object],Ecological validity at the cost of internal validity,[object Object],[Ackerman 2000], [Barkhuus and Rode 2007], [Chi 2009], [Greenberg and Buxton 2008], [Kaye and Sengers 2007], [Landay 2009], [Lieberman 2003], [Olsen 2007], [Zhai 2003],[object Object]
sociotechnical,[object Object],studiersbuilders,[object Object],Show us elegant complexity.(simple ideas that enable complex scenarios),[object Object],We let people type messages up to 140 characters.,[object Object],That’s it? What is possible now that wasn’t before?,[object Object],Nothing — but focus on emergent social activity.,[object Object],Can you add multitouch?,[object Object],Not using IE8.,[object Object]
socio  technical,[object Object],studiers  builders,[object Object],Build a technically interesting system ,[object Object],    (that is hard to spread or evaluate), or,[object Object],Simplify to a system with socially interesting outcomes ,[object Object],    (that builders find less novel).,[object Object]
The contribution needs to take one strong stance or another. Either it describes a novel system or a novel social interaction. If it’s a system, then I question the novelty. If it’s a social interaction, it needs more development.,[object Object],– CHI metareviewer, paraphrased,[object Object],Build a technically interesting system ,[object Object],    (that is hard to spread or evaluate), or,[object Object],Simplify to a system with socially interesting outcomes ,[object Object],    (that builders find less novel).,[object Object]
Create a shared understanding,[object Object],of research contributions,[object Object]
social technical,[object Object],New forms of social interaction,[object Object],	Shared organizational memory [Ackerman 1994],[object Object],Designs that impact social interactions,[object Object],Increasing online contribution [Beenen et al. 2004],[object Object],Enable fluent social interaction in a new domain,[object Object],Socially translucent systems [Erickson and Kellogg 2000],[object Object]
social technical,[object Object],Designs collecting or powered by social data,[object Object],Wikidashboard [Suh et al. 2008]; sense.us [Heeret al. 2007],[object Object],Algorithms to coordinate crowds or derive signal from social data,[object Object],Collaborative Filtering [Resnick et al. 1994]; Iterate-and-Vote [Little et al. 2010],[object Object],Platforms and infrastructures,[object Object],TurKit [Little et al. 2010],[object Object]
social technical,[object Object],×,[object Object],Paired contributions can increase each others’ value,[object Object],ManyEyes,[object Object],[Viégas et al. 2007],[object Object]
In conclusion introduction:,[object Object],What are our millennium challenges? ,[object Object],What is our relationship with industry 	and walled gardens?,[object Object],How can (and should) we evolve,[object Object],our standards of proof?,[object Object]
Michael S. Bernstein, Mark S. Ackerman, Ed H. Chi, Robert C. Miller,[object Object]
The Trouble with Social Computing Systems Research

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The Trouble with Social Computing Systems Research

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Notas del editor

  1. The rise of social computing is impacting SIGCHI research immensely. Wikipedia, Twitter, Delicious, and Mechanical Turk have helped us begin to understand people and their interactions through large, naturally occurring datasets. Computational social science will only grow in the coming years.
  2. Likewise, those invested in the systems research community in social computing hope for a trajectory of novel, impactful sociotechnical designs. By systems research, we mean research whose main contribution is the presentation of a new sociotechnical artifact, algorithm, design, or platform. Systems research in social computing is valuable because it envisions new ways of interacting with social systems, and spreads these ideas to other researchers and the world at large. This broader focus, married with a massive growth in platforms, APIs, and interest in social computing, might suggest that we will see lots of new interesting research systems.
  3. This 4:1 ratio may reflect overall submission ratios to CHI, represent a steady state and not a trend, or equalize out in the long term in terms of highly-cited papers. However, a 4:1 ratio is still worrying: a perceived publication bias might push new researchers capable of both types of work to study systems rather than build them. If this happens en masse, it might threaten our field’s ability to look forward.
  4. Note these are not my papers
  5. I’m going to argue that ____.
  6. Memorable technical contributions are simple ideas that enable interesting, complex scenarios. Systems demos will thus target flashy tasks, aim years ahead of the technology adoption curve, or assume technically literate (often expert) users. For example, end user programming, novel interaction techniques, and augmented reality research all make assumptions about Moore’s Law, adoption, or user training.
  7. Memorable technical contributions are simple ideas that enable interesting, complex scenarios. Systems demos will thus target flashy tasks, aim years ahead of the technology adoption curve, or assume technically literate (often expert) users. For example, end user programming, novel interaction techniques, and augmented reality research all make assumptions about Moore’s Law, adoption, or user training.
  8. Memorable technical contributions are simple ideas that enable interesting, complex scenarios. Systems demos will thus target flashy tasks, aim years ahead of the technology adoption curve, or assume technically literate (often expert) users. For example, end user programming, novel interaction techniques, and augmented reality research all make assumptions about Moore’s Law, adoption, or user training.
  9. Memorable technical contributions are simple ideas that enable interesting, complex scenarios. Systems demos will thus target flashy tasks, aim years ahead of the technology adoption curve, or assume technically literate (often expert) users. For example, end user programming, novel interaction techniques, and augmented reality research all make assumptions about Moore’s Law, adoption, or user training.
  10. Memorable technical contributions are simple ideas that enable interesting, complex scenarios. Systems demos will thus target flashy tasks, aim years ahead of the technology adoption curve, or assume technically literate (often expert) users. For example, end user programming, novel interaction techniques, and augmented reality research all make assumptions about Moore’s Law, adoption, or user training.
  11. Memorable technical contributions are simple ideas that enable interesting, complex scenarios. Systems demos will thus target flashy tasks, aim years ahead of the technology adoption curve, or assume technically literate (often expert) users. For example, end user programming, novel interaction techniques, and augmented reality research all make assumptions about Moore’s Law, adoption, or user training.