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Colliding Computer & Social Sciences at the VU

Source: Think Links

One of the things that I think is great about the VU (Vrije Universiteit Amsterdam) where I work is the promotion of interdisciplinary work through organizations like the Network Institute.  Computer Science is often known for interacting with biology, physics, and economics but we are now seeing the application of computing to Social Science problems. This is great for CS because domains often introduce new fundamental CS problems.

To talk about the overlap and potential opportunities for greater Social Science and Computer Science collaboration at the VU, Iina Hellsten (from Organization Science) and I organized a half-day symposium on Tuesday, June 29, 2010. We had a great environment for the discussion in the Intertain Lab (a space for investigating new interactive environments).

We had 17 participants about half from the Social Sciences (covering organization science, communication science, to psychology)  and half from Computer Science.

We started off with talks setting the scene from myself (on the CS side) and Peter Groenewegen and then moved to a series of shorter talks giving us a glimpse of the different focuses of some of the attendees. Even during these talks, there was clearly excitement about the possibilities for collaboration and there were several interesting conversations about the work itself.

The last part of the symposium was a session where we identified challenges and opportunities. We ran this as a post-it note session where each participant wrote two challenges and two opportunities on post it notes. (I got this idea from Katy Börner at her NSF Workshop on Mapping of Science and the Semantic Web. Thanks Katy!). Amazingly, these post-it notes always cluster together. Below is an image of the results of the session:

The group identified 8 different groupings of the 60 challenges and opportunities listed by the participants. They were:

  1. How do we bridge the vocabulary gap between social science and computer science?
  2. We have the opportunity to build new applications using insights from social science.
  3. Writing new proposals and fundraising.
  4. Knowing who in the other discipline is working on a particular subject and maintaining connections between the disciplines.
  5. Being able to answer new research questions.
  6. Having an opportunity to apply research results in the “real world”.
  7. Automating parts of social science analysis (think network extraction from data sets).
  8. Overcoming the differing research styles of the two disciplines especially in terms of publication cycles.

Below we list the actual text of the post-it notes grouped into the 8 areas.

The outcome of the symposium is that now that we’ve identified clusters of challenges and opportunities, we need to focus on concrete collaborations to address these areas. We will hold another session in September to discuss concrete actions.

Overall, this event showed me that at the VU, we have both the right structures but the right people to engage in this sort of interdisciplinary research.

Results of Post-it Note Session:
post-it content challenge or opportunity (c/o) category
More user centered/friendly systems. Not only usability, but also privacy strong communication ties o no category
convience peers (e.g reviewers) c no category
learn to give data (LOD) the right intrepretation o no category
use the methodological rigor (of social science?) to scope your results o no category
exploring/studying area for “design” of techno-social systems o vocab
seduce social scentists to think technical and computer scientist to think social c vocab
mix technical(cs) and social theoris and modes to advance understanding c vocab
deal with some fuzziness of social science models c vocab
time consuming coordination or alternatively miscommunication c vocab
different mindsets conceptualizations c vocab
it is difficult to develop shared understanding of theory c vocab
it is difficult to find common levels of abstraction c vocab
integrate low level network analysis with higher level models from social sciences c vocab
different sorts of thinking in cs and social social science c vocab
combining conceptual work to “bridge” the gap c vocab
very different outlook on research c vocab
speaking/interacting using the “same” vocabulary c vocab
finding coomon language between computer & social sciences c vocab
talk similar language c vocab
new applications of technology o new apps
teaching each other concepts/methods o new apps
developing new technology bundles together (e.g. pda-based surveys) o new apps
processing huge bulks of data o new apps
fundrasiing opportunities o funds
socio-technical support for agile social networks in organizations o funds
cross-polinization & cross-fertilization for developing meaningful insights o funds
keeping the connections across exisiting projects c who’s who
knowing who is doing what c who’s who
give overview of who is doing what in this field at the VU (via webpage?) o who’s who
identify the true webscience problems in the convergence of cs & ss o answering new questions
find relevant problems that are now solvable because of ICT solutions o answering new questions
generating new ideas o answering new questions
seeing research problems from new perspectives o answering new questions
provide overview of available methods, etc. o answering new questions
if we work together we can integrate our knoweldge and get a better idea about the big picture o answering new questions
make technical & interpretive knowledge come together o answering new questions
designing studies that have a greate change of producing real insights o real results
understand the social web phenomena like wikipedia, facebook (motivation/quality) o real results
share (experience) tools for network vizualization & analysis o real results
linking concepts that wouldn’t have been associated earlier (underlying frames) o real results
applying the results of the detailed tracking of people o real results
ending up with a lot of manual work to compensate for technical errors c automated analysis
combining social networks and content networks o automated analysis
automating social and content analysis o automated analysis
losing valuable information that might be essential to understanding phenomena c automated analysis
automated analysis & interpretations of social phenomena c automated analysis
thinking that one side (your side) always does things “the right way”. c research styles
interests are divergent c research styles
research timeframes are divergent c research styles
cs need short-term “help” -> pulbication cycle c research styles
different scientific approaches and styles (e.g. publication) c research styles

Filed under: academia Tagged: computational social science, post-it notes, symposium, vu unviersity amsterdam